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Boelders SM, De Baene W, Postma E, Gehring K, Ong LL. Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space. Neuroinformatics 2024:10.1007/s12021-024-09671-9. [PMID: 38900230 DOI: 10.1007/s12021-024-09671-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.
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Affiliation(s)
- S M Boelders
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - W De Baene
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands
| | - E Postma
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - K Gehring
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands.
| | - L L Ong
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
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2
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Horng H, Scott C, Winham S, Jensen M, Pantalone L, Mankowski W, Kerlikowske K, Vachon CM, Kontos D, Shinohara RT. Multivariate testing and effect size measures for batch effect evaluation in radiomic features. Sci Rep 2024; 14:13923. [PMID: 38886407 PMCID: PMC11183083 DOI: 10.1038/s41598-024-64208-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
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Affiliation(s)
- Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | | | | | | | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Walter Mankowski
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Columbia University, New York, NY, 10027, USA
| | - Russell T Shinohara
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
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3
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Kazerooni AF, Khalili N, Liu X, Haldar D, Jiang Z, Anwar SM, Albrecht J, Adewole M, Anazodo U, Anderson H, Bagheri S, Baid U, Bergquist T, Borja AJ, Calabrese E, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Haldar S, Iglesias JE, Janas A, Johansen E, Jones BV, Kofler F, LaBella D, Lai HA, Van Leemput K, Li HB, Maleki N, McAllister AS, Meier Z, Menze B, Moawad AW, Nandolia KK, Pavaine J, Piraud M, Poussaint T, Prabhu SP, Reitman Z, Rodriguez A, Rudie JD, Shaikh IS, Shah LM, Sheth N, Shinohara RT, Tu W, Viswanathan K, Wang C, Ware JB, Wiestler B, Wiggins W, Zapaishchykova A, Aboian M, Bornhorst M, de Blank P, Deutsch M, Fouladi M, Hoffman L, Kann B, Lazow M, Mikael L, Nabavizadeh A, Packer R, Resnick A, Rood B, Vossough A, Bakas S, Linguraru MG. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). ARXIV 2024:arXiv:2305.17033v7. [PMID: 37292481 PMCID: PMC10246083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Xinyang Liu
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington DC, USA
| | - Debanjan Haldar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Thomas Jefferson University Hospital, PA, USA
| | - Zhifan Jiang
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington DC, USA
| | - Syed Muhammed Anwar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington DC, USA
| | | | - Maruf Adewole
- Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria
| | - Udunna Anazodo
- Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ujjwal Baid
- Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Austin J. Borja
- Department of Neurosurgery at the University of Southern California, CA, USA
| | - Evan Calabrese
- Department of Radiology, Duke University Medical Center, USA
| | | | | | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shuvanjan Haldar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Biomedical Engineering Rutgers University, New Brunswick, NJ, USA
| | - Juan Eugenio Iglesias
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Elaine Johansen
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | | | - Dominic LaBella
- Department of Radiation Oncology, Duke University Medical Center, USA
| | - Hollie Anne Lai
- Department of Radiology, Children’s Health Orange County, CA, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | - Hongwei Bran Li
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | - Bjoern Menze
- Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | | | - Khanak K Nandolia
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Rishikesh, India
| | - Julija Pavaine
- Department of Paediatric Radiology, Royal Manchester Children’s Hospital, Manchester University Hospitals NHS Foundation Trust, University of Manchester, Manchester, UK
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | | | - Tina Poussaint
- Dana-Farber Brigham Cancer Center & Boston Children’s Hospital, Boston, MA, USA
| | - Sanjay P Prabhu
- Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Zachary Reitman
- Department of Radiation Oncology, Duke University Medical Center, USA
| | - Andres Rodriguez
- Department of Diagnostic & Interventional Imaging, Neuroradiology section. The University of Texas Health Sciences Center at Houston
| | | | | | | | - Nakul Sheth
- Department of Radiology, The Children’s Hospital of Philadelphia, PA, USA
| | - Russel Taki Shinohara
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Wenxin Tu
- College of Arts and Sciences, University of Pennsylvania, PA, USA
| | - Karthik Viswanathan
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, USA
| | - Jeffrey B Ware
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Walter Wiggins
- Department of Radiology, Duke University Medical Center, USA
| | - Anna Zapaishchykova
- Dana-Farber Brigham Cancer Center & Boston Children’s Hospital, Boston, MA, USA
| | | | - Miriam Bornhorst
- Brain Tumor Institute, Children’s National Hospital, Washington, DC, USA
| | - Peter de Blank
- Brain Tumor Center, Cincinnati Children’s Hospital, Cincinnati, OH, USA
| | - Michelle Deutsch
- Pediatric Neuro-Oncology Program, Nationwide Children’s Hospital, Columbus, OH, US
| | - Maryam Fouladi
- Pediatric Neuro-Oncology Program, Nationwide Children’s Hospital, Columbus, OH, US
| | - Lindsey Hoffman
- Center for Cancer and Blood Disorders, Phoenix Children’s Hospital, Phoenix, AZ, US
| | - Benjamin Kann
- Dana-Farber Brigham Cancer Center & Boston Children’s Hospital, Boston, MA, USA
| | - Margot Lazow
- Pediatric Neuro-Oncology Program, Nationwide Children’s Hospital, Columbus, OH, US
| | - Leonie Mikael
- Pediatric Neuro-Oncology Program, Nationwide Children’s Hospital, Columbus, OH, US
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roger Packer
- Brain Tumor Institute, Children’s National Hospital, Washington, DC, USA
| | - Adam Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Rood
- Center for Cancer and Blood Disorders, Children’s National Hospital, Washington, DC, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, The Children’s Hospital of Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington DC, USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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4
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Morrison S, Gatsonis C, Eloyan A, Steingrimsson JA. Survival analysis using deep learning with medical imaging. Int J Biostat 2024; 20:1-12. [PMID: 37312249 PMCID: PMC11074924 DOI: 10.1515/ijb-2022-0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 02/24/2023] [Indexed: 06/15/2023]
Abstract
There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.
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Affiliation(s)
- Samantha Morrison
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
| | - Constantine Gatsonis
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
| | - Ani Eloyan
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
| | - Jon Arni Steingrimsson
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
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5
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Fatania K, Frood R, Mistry H, Short SC, O’Connor J, Scarsbrook AF, Currie S. Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study. Cancers (Basel) 2024; 16:1301. [PMID: 38610979 PMCID: PMC11011077 DOI: 10.3390/cancers16071301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.
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Affiliation(s)
- Kavi Fatania
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Russell Frood
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Hitesh Mistry
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (H.M.)
| | - Susan C. Short
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
- Department of Oncology, Leeds Teaching Hospitals NHS Trust, St James’s University Hospital, Leeds LS9 7TF, UK
| | - James O’Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (H.M.)
- Department of Radiology, The Christie Hospital, Manchester M20 4BX, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK
| | - Andrew F. Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Stuart Currie
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
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6
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Guo J, Fathi Kazerooni A, Toorens E, Akbari H, Yu F, Sako C, Mamourian E, Shinohara RT, Koumenis C, Bagley SJ, Morrissette JJD, Binder ZA, Brem S, Mohan S, Lustig RA, O'Rourke DM, Ganguly T, Bakas S, Nasrallah MP, Davatzikos C. Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach. Sci Rep 2024; 14:4922. [PMID: 38418494 PMCID: PMC10902376 DOI: 10.1038/s41598-024-55072-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 02/19/2024] [Indexed: 03/01/2024] Open
Abstract
Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.
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Affiliation(s)
- Jun Guo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erik Toorens
- Penn Genomic Analysis Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA, USA
| | - Fanyang Yu
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging and Visualization (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer J D Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Tapan Ganguly
- Penn Genomic Analysis Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - MacLean P Nasrallah
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Kumar D, Suthar N. Predictive analytics and early intervention in healthcare social work: a scoping review. SOCIAL WORK IN HEALTH CARE 2024; 63:208-229. [PMID: 38349783 DOI: 10.1080/00981389.2024.2316700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/05/2024] [Indexed: 02/15/2024]
Abstract
This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain.
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Affiliation(s)
- Dinesh Kumar
- Faculty of Business and Applied Arts, Lovely Professional University, Mittal School of Business, Phagwara, India
| | - Nidhi Suthar
- Administration, Pomento IT Services, Hisar, India
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8
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Yu F, Wang R, Chaudhari P, Davatzikos C. Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12927:129271F. [PMID: 38650741 PMCID: PMC11034818 DOI: 10.1117/12.3005434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.
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Affiliation(s)
- Fanyang Yu
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rongguang Wang
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pratik Chaudhari
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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9
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Tan SH, Guan CA, Bujang MA, Lai WH, Voon PJ, Sim EUH. Identification of phenomic data in the pathogenesis of cancers of the gastrointestinal (GI) tract in the UK biobank. Sci Rep 2024; 14:1997. [PMID: 38263244 PMCID: PMC10805853 DOI: 10.1038/s41598-024-52421-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/18/2024] [Indexed: 01/25/2024] Open
Abstract
Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37-73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23-2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation.
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Affiliation(s)
- Shirin Hui Tan
- Clinical Research Centre, Sarawak General Hospital, Ministry of Health Malaysia, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia.
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Malaysia.
| | - Catherina Anak Guan
- Clinical Research Centre, Sarawak General Hospital, Ministry of Health Malaysia, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Mohamad Adam Bujang
- Clinical Research Centre, Sarawak General Hospital, Ministry of Health Malaysia, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Wei Hong Lai
- Clinical Research Centre, Sarawak General Hospital, Ministry of Health Malaysia, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Pei Jye Voon
- Department of Radiotherapy, Oncology and Palliative Care, Sarawak General Hospital, Ministry of Health Malaysia, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Edmund Ui Hang Sim
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Malaysia
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10
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Arrington D, Motley R, Colbert ZM, Lehman M, Ramachandran P. PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction. J Med Phys 2024; 49:12-21. [PMID: 38828062 PMCID: PMC11141757 DOI: 10.4103/jmp.jmp_120_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/27/2023] [Accepted: 01/07/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. Aims To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Materials and Methods Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. Results The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. Conclusion The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.
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Affiliation(s)
- Daniel Arrington
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Ryan Motley
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Zachery Morton Colbert
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Margot Lehman
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Prabhakar Ramachandran
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
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11
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Saeed N, Ridzuan M, Alasmawi H, Sobirov I, Yaqub M. MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. Med Image Anal 2023; 90:102989. [PMID: 37827111 DOI: 10.1016/j.media.2023.102989] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/24/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023]
Abstract
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medical efficacy. From this perspective, we examine a wide range of deep learning algorithms for the assessment of glioblastoma - a common brain tumor in older adults that is lethal. Surgery, chemotherapy, and radiation are the standard treatments for glioblastoma patients. The methylation status of the MGMT promoter, a specific genetic sequence found in the tumor, affects chemotherapy's effectiveness. MGMT promoter methylation improves chemotherapy response and survival in several cancers. MGMT promoter methylation is determined by a tumor tissue biopsy, which is then genetically tested. This lengthy and invasive procedure increases the risk of infection and other complications. Thus, researchers have used deep learning models to examine the tumor from brain MRI scans to determine the MGMT promoter's methylation state. We employ deep learning models and one of the largest public MRI datasets of 585 participants to predict the methylation status of the MGMT promoter in glioblastoma tumors using MRI scans. We test these models using Grad-CAM, occlusion sensitivity, feature visualizations, and training loss landscapes. Our results show no correlation between these two, indicating that external cohort data should be used to verify these models' performance to assure the accuracy and reliability of deep learning systems in cancer diagnosis.
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Affiliation(s)
- Numan Saeed
- Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates.
| | - Muhammad Ridzuan
- Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Hussain Alasmawi
- Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Ikboljon Sobirov
- Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Mohammad Yaqub
- Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
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12
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Ruffle JK, Mohinta S, Pombo G, Gray R, Kopanitsa V, Lee F, Brandner S, Hyare H, Nachev P. Brain tumour genetic network signatures of survival. Brain 2023; 146:4736-4754. [PMID: 37665980 PMCID: PMC10629773 DOI: 10.1093/brain/awad199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 09/06/2023] Open
Abstract
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Robert Gray
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Valeriya Kopanitsa
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Faith Lee
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Sebastian Brandner
- Division of Neuropathology and Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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13
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Zhou Z, Qian X, Hu J, Chen G, Zhang C, Zhu J, Dai Y. An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study. Quant Imaging Med Surg 2023; 13:7504-7522. [PMID: 37969634 PMCID: PMC10644131 DOI: 10.21037/qims-23-20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 06/12/2023] [Indexed: 11/17/2023]
Abstract
Background Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Guangqiang Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Caiyuan Zhang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Suzhou Guoke Kangcheng Medical Technology Co., Ltd., Suzhou, China
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14
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Bianconi A, Rossi LF, Bonada M, Zeppa P, Nico E, De Marco R, Lacroce P, Cofano F, Bruno F, Morana G, Melcarne A, Ruda R, Mainardi L, Fiaschi P, Garbossa D, Morra L. Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment. Brain Inform 2023; 10:26. [PMID: 37801128 PMCID: PMC10558414 DOI: 10.1186/s40708-023-00207-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/16/2023] [Indexed: 10/07/2023] Open
Abstract
OBJECTIVE Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. METHODS The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). RESULTS In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. CONCLUSIONS The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma's MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.
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Affiliation(s)
- Andrea Bianconi
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy.
| | | | - Marta Bonada
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Pietro Zeppa
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Elsa Nico
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Raffaele De Marco
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | | | - Fabio Cofano
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Francesco Bruno
- Neurooncology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Giovanni Morana
- Neuroradiology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Antonio Melcarne
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Roberta Ruda
- Neurooncology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Luca Mainardi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Pietro Fiaschi
- IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
- Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili, Univeristy of Genoa, Genoa, Italy
| | - Diego Garbossa
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy
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15
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Familiar AM, Kazerooni AF, Anderson H, Lubneuski A, Viswanathan K, Breslow R, Khalili N, Bagheri S, Haldar D, Kim MC, Arif S, Madhogarhia R, Nguyen TQ, Frenkel EA, Helili Z, Harrison J, Farahani K, Linguraru MG, Bagci U, Velichko Y, Stevens J, Leary S, Lober RM, Campion S, Smith AA, Morinigo D, Rood B, Diamond K, Pollack IF, Williams M, Vossough A, Ware JB, Mueller S, Storm PB, Heath AP, Waanders AJ, Lilly J, Mason JL, Resnick AC, Nabavizadeh A. A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network. ARXIV 2023:arXiv:2310.01413v1. [PMID: 38106459 PMCID: PMC10723526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.
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Affiliation(s)
- Ariana M. Familiar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aliaksandr Lubneuski
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Karthik Viswanathan
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rocky Breslow
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Debanjan Haldar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Meen Chul Kim
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sherjeel Arif
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rachel Madhogarhia
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Thinh Q. Nguyen
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elizabeth A. Frenkel
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Zeinab Helili
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jessica Harrison
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ulas Bagci
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yury Velichko
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jeffrey Stevens
- Department of Hematology and Oncology, Seattle Children’s, Seattle, WA, USA
| | - Sarah Leary
- Department of Hematology and Oncology, Seattle Children’s, Seattle, WA, USA
| | - Robert M. Lober
- Division of Neurosurgery, Dayton Children’s Hospital, Dayton, OH, USA
| | - Stephani Campion
- Department of Pediatric Hematology & Oncology, Orlando Health Arnold Palmer Hospital for Children, Orlando, FL, USA
| | - Amy A. Smith
- Department of Pediatric Hematology & Oncology, Orlando Health Arnold Palmer Hospital for Children, Orlando, FL, USA
| | - Denise Morinigo
- Department of Hematology-Oncology, Children’s National Hospital, Washington, DC, USA
| | - Brian Rood
- Department of Hematology-Oncology, Children’s National Hospital, Washington, DC, USA
| | - Kimberly Diamond
- Department of Pediatric Neurosurgery, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Ian F. Pollack
- Department of Pediatric Neurosurgery, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Melissa Williams
- Division of Hematology, Oncology, NeuroOncology, and Transplant, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jeffrey B. Ware
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sabine Mueller
- Department of Neurology, Division of Child Neurology, University of San Francisco, San Francisco, CA, USA
| | - Phillip B. Storm
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Allison P. Heath
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Angela J. Waanders
- Division of Hematology, Oncology, NeuroOncology, and Transplant, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jena Lilly
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jennifer L. Mason
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam C. Resnick
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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16
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Ghosh A, Yekeler E, Teixeira SR, Dalal D, States L. Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas. Eur Radiol 2023; 33:6726-6735. [PMID: 37178203 DOI: 10.1007/s00330-023-09628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/18/2023] [Accepted: 02/26/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas. METHODS A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set. RESULTS The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%. CONCLUSION The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models. KEY POINTS • MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Cincinnati Children's Hospital and Medical Centre, Cincinnati, OH, USA.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Ensar Yekeler
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sara Reis Teixeira
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Deepa Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa States
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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17
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Cepeda S, García-García S, Arrese I, Herrero F, Escudero T, Zamora T, Sarabia R. The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM). Data Brief 2023; 50:109617. [PMID: 37808543 PMCID: PMC10551826 DOI: 10.1016/j.dib.2023.109617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.
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Affiliation(s)
- Santiago Cepeda
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Sergio García-García
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Francisco Herrero
- Department of Radiology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Trinidad Escudero
- Department of Radiology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Tomás Zamora
- Department of Pathology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
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18
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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19
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Rezaeijo SM, Chegeni N, Baghaei Naeini F, Makris D, Bakas S. Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans. Cancers (Basel) 2023; 15:3565. [PMID: 37509228 PMCID: PMC10377568 DOI: 10.3390/cancers15143565] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.
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Affiliation(s)
- Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Nahid Chegeni
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Fariborz Baghaei Naeini
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK
| | - Dimitrios Makris
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK
| | - Spyridon Bakas
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK
- Richards Medical Research Laboratories, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Floor 7, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
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20
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Li HB, Conte GM, Anwar SM, Kofler F, Ezhov I, van Leemput K, Piraud M, Diaz M, Cole B, Calabrese E, Rudie J, Meissen F, Adewole M, Janas A, Kazerooni AF, LaBella D, Moawad AW, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Johanson E, Meier Z, Davatzikos C, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh HM, Wiest R, Kirschke J, Colen RR, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber MA, Mahajan A, Mohan S, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Colak E, Crivellaro P, Jakab A, Albrecht J, Anazodo U, Aboian M, Yu T, Chung V, Bergquist T, Eddy J, Albrecht J, Baid U, Bakas S, Linguraru MG, Menze B, Iglesias JE, Wiestler B. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn). ARXIV 2023:arXiv:2305.09011v5. [PMID: 37608932 PMCID: PMC10441440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.
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Affiliation(s)
- Hongwei Bran Li
- University of Zurich, Switzerland
- Department of Informatics, Technical University Munich, Germany
- Klinikum rechts der Isar, Technical University of Munich, Germany
| | | | - Syed Muhammad Anwar
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Florian Kofler
- Helmholtz AI, Helmholtz Munich, Germany
- Department of Informatics, Technical University Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | | | | | | | | | - Evan Calabrese
- Duke University Medical Center, Department of Radiology, USA
- University of California San Francisco, CA, USA
| | - Jeff Rudie
- University of California San Francisco, CA, USA
| | - Felix Meissen
- Department of Informatics, Technical University Munich, Germany
| | - Maruf Adewole
- Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria
| | | | - Anahita Fathi Kazerooni
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Dominic LaBella
- Duke University Medical Center, Department of Radiation Oncology, USA
| | | | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | | | | | | | - Russell Takeshi Shinohara
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Walter Wiggins
- Duke University Medical Center, Department of Radiology, USA
| | - Zachary Reitman
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Chunhao Wang
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Xinyang Liu
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Zhifan Jiang
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Ariana Familiar
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Freymann
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Justin Kirby
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Michel Bilello
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Roland Wiest
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
- Support Centre for Advanced Neuroimaging Inselspital, Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Bern, Switzerland
| | - Jan Kirschke
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Rivka R Colen
- University of Pittsburgh Medical Center, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Daniel Marcus
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Mikhail Milchenko
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Arash Nazeri
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057 Rostock, Germany
| | - Abhishek Mahajan
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Suyash Mohan
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Mongan
- University of California San Francisco, CA, USA
| | | | - Soonmee Cha
- University of California San Francisco, CA, USA
| | | | | | | | | | | | - Udunna Anazodo
- Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | | | - Thomas Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, USA
| | | | | | | | | | - Ujjwal Baid
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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21
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Chakrabarty S, Abidi SA, Mousa M, Mokkarala M, Hren I, Yadav D, Kelsey M, LaMontagne P, Wood J, Adams M, Su Y, Thorpe S, Chung C, Sotiras A, Marcus DS. Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO). JCO Clin Cancer Inform 2023; 7:e2200177. [PMID: 37146265 PMCID: PMC10281444 DOI: 10.1200/cci.22.00177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/25/2023] [Accepted: 03/06/2023] [Indexed: 05/07/2023] Open
Abstract
PURPOSE Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. MATERIALS AND METHODS Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas. RESULTS The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively. CONCLUSION This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.
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Affiliation(s)
- Satrajit Chakrabarty
- Department of Electrical and Systems Engineering, Washington University in St Louis, St Louis, MO
| | - Syed Amaan Abidi
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Mina Mousa
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Mahati Mokkarala
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Isabelle Hren
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO
| | - Divya Yadav
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Matthew Kelsey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - John Wood
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Adams
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yuzhuo Su
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sherry Thorpe
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Caroline Chung
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
- Institute for Informatics, Washington University School of Medicine, St Louis, MO
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
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22
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Robinet L, Siegfried A, Roques M, Berjaoui A, Cohen-Jonathan Moyal E. MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation. Cancers (Basel) 2023; 15:cancers15082253. [PMID: 37190181 DOI: 10.3390/cancers15082253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
Glioblastoma is the most aggressive primary brain tumor, which almost systematically relapses despite surgery (when possible) followed by radio-chemotherapy temozolomide-based treatment. Upon relapse, one option for treatment is another chemotherapy, lomustine. The efficacy of these chemotherapy regimens depends on the methylation of a specific gene promoter known as MGMT, which is the main prognosis factor for glioblastoma. Knowing this biomarker is a key issue for the clinician to personalize and adapt treatment to the patient at primary diagnosis for elderly patients, in particular, and also upon relapse. The association between MRI-derived information and the prediction of MGMT promoter status has been discussed in many studies, and some, more recently, have proposed the use of deep learning algorithms on multimodal scans to extract this information, but they have failed to reach a consensus. Therefore, in this work, beyond the classical performance figures usually displayed, we seek to compute confidence scores to see if a clinical application of such methods can be seriously considered. The systematic approach carried out, using different input configurations and algorithms as well as the exact methylation percentage, led to the following conclusion: current deep learning methods are unable to determine MGMT promoter methylation from MRI data.
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Affiliation(s)
- Lucas Robinet
- IRT Saint-Exupéry, 31400 Toulouse, France
- IUCT-Oncopole-Institut Claudius Regaud, 31100 Toulouse, France
- INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT), University Paul Sabatier Toulouse III, 31100 Toulouse, France
| | - Aurore Siegfried
- INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT), University Paul Sabatier Toulouse III, 31100 Toulouse, France
- Pathology and Cytology Department, CHU Toulouse, IUCT Oncopole, 31100 Toulouse, France
| | - Margaux Roques
- Department of Neuroradiology, Hopital Pierre Paul Riquet, CHU Purpan, 31300 Toulouse, France
| | | | - Elizabeth Cohen-Jonathan Moyal
- IUCT-Oncopole-Institut Claudius Regaud, 31100 Toulouse, France
- INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT), University Paul Sabatier Toulouse III, 31100 Toulouse, France
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23
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Pacheco BM, Cassia GDSE, Silva D. Towards fully automated deep-learning-based brain tumor segmentation: Is brain extraction still necessary? Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Bornes R, Montero J, Correia A, Marques T, Rosa N. Peri-implant diseases diagnosis, prognosis and dental implant monitoring: a narrative review of novel strategies and clinical impact. BMC Oral Health 2023; 23:183. [PMID: 36997949 PMCID: PMC10061972 DOI: 10.1186/s12903-023-02896-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/17/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND The diagnosis of peri-implantar and periodontal relies mainly on a set of clinical measures and the evaluation of radiographic images. However, these clinical settings alone are not sufficient to determine, much less predict, periimplant bone loss or future implant failure. Early diagnosis of periimplant diseases and its rate of progress may be possible through biomarkers assessment. Once identified, biomarkers of peri-implant and periodontal tissue destruction may alert the clinicians before clinical signs show up. Therefore, it is important to consider developing chair-side diagnostic tests with specificity for a particular biomarker, indicating the current activity of the disease. METHODS A search strategy was created at Pubmed and Web of Science to answer the question: "How the molecular point-of-care tests currently available can help in the early detection of peri-implant diseases and throws light on improvements in point of care diagnostics devices?" RESULTS The PerioSafe® PRO DRS (dentognostics GmbH, Jena) and ImplantSafe® DR (dentognostics GmbH, Jena ORALyzer® test kits, already used clinically, can be a helpful adjunct tool in enhancing the diagnosis and prognosis of periodontal/peri-implantar diseases. With the advances of sensor technology, the biosensors can perform daily monitoring of dental implants or periodontal diseases, making contributions to personal healthcare and improve the current status quo of health management and human health. CONCLUSIONS Based on the findings, more emphasis is given to the role of biomarkers in diagnosing and monitoring periodontal and peri-implant diseases. By combining these strategies with traditional protocols, professionals could increase the accuracy of early detection of peri-implant and periodontal diseases, predicting disease progression, and monitoring of treatment outcomes.
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Affiliation(s)
- Rita Bornes
- Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Universidade Católica Portuguesa, Viseu, Portugal.
| | - Javier Montero
- Department of Surgery, Faculty of Medicine, University of Salamanca, Salamanca, Spain
| | - André Correia
- Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Universidade Católica Portuguesa, Viseu, Portugal
| | - Tiago Marques
- Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Universidade Católica Portuguesa, Viseu, Portugal
| | - Nuno Rosa
- Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Universidade Católica Portuguesa, Viseu, Portugal
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Chitalia R, Miliotis M, Jahani N, Tastsoglou S, McDonald ES, Belenky V, Cohen EA, Newitt D, Van't Veer LJ, Esserman L, Hylton N, DeMichele A, Hatzigeorgiou A, Kontos D. Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy. COMMUNICATIONS MEDICINE 2023; 3:46. [PMID: 36997615 PMCID: PMC10063641 DOI: 10.1038/s43856-023-00273-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 03/10/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS). METHODS A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components. RESULTS We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002). CONCLUSIONS These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.
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Affiliation(s)
- Rhea Chitalia
- Department of Bioengineering, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Marios Miliotis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Nariman Jahani
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Spyros Tastsoglou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Elizabeth S McDonald
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Vivian Belenky
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Eric A Cohen
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Laura J Van't Veer
- Department of Surgery and Oncology, University of California, San Francisco, USA
| | - Laura Esserman
- Department of Surgery and Oncology, University of California, San Francisco, USA
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Angela DeMichele
- Department of Medicine, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Artemis Hatzigeorgiou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Despina Kontos
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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26
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Cepeda S, Luppino LT, Pérez-Núñez A, Solheim O, García-García S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jiménez-Roldán L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel) 2023; 15:1894. [PMID: 36980783 PMCID: PMC10047582 DOI: 10.3390/cancers15061894] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.
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Affiliation(s)
- Santiago Cepeda
- Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain
| | - Luigi Tommaso Luppino
- Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
| | - Angel Pérez-Núñez
- Department of Neurosurgery, 12 de Octubre University Hospital (i+12), 28041 Madrid, Spain
- Department of Surgery, School of Medicine, Complutense University, 28040 Madrid, Spain
- Instituto de Investigación Sanitaria, 12 de Octubre University Hospital (i+12), 28041 Madrid, Spain
| | - Ole Solheim
- Department of Neurosurgery, St. Olavs University Hospital, 7030 Trondheim, Norway
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Sergio García-García
- Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain
| | | | - Anna Karlberg
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
| | - Rosario Sarabia
- Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain
| | - Tomás Zamora
- Department of Pathology, Río Hortega University Hospital, 47014 Valladolid, Spain
| | - Pedro Gonzalez
- Department of Neurosurgery, 12 de Octubre University Hospital (i+12), 28041 Madrid, Spain
| | - Luis Jiménez-Roldán
- Department of Neurosurgery, 12 de Octubre University Hospital (i+12), 28041 Madrid, Spain
- Department of Surgery, School of Medicine, Complutense University, 28040 Madrid, Spain
- Instituto de Investigación Sanitaria, 12 de Octubre University Hospital (i+12), 28041 Madrid, Spain
| | - Samuel Kuttner
- Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
- The PET Imaging Center, University Hospital of North Norway, 9019 Tromsø, Norway
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27
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Qureshi SA, Hussain L, Ibrar U, Alabdulkreem E, Nour MK, Alqahtani MS, Nafie FM, Mohamed A, Mohammed GP, Duong TQ. Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. Sci Rep 2023; 13:3291. [PMID: 36841898 PMCID: PMC9961309 DOI: 10.1038/s41598-023-30309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/21/2023] [Indexed: 02/27/2023] Open
Abstract
Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.
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Affiliation(s)
- Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
| | - Lal Hussain
- Department of Computer Science and IT, Neelum Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan. .,Department of Computer Science and IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan. .,Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY, 10467, USA.
| | - Usama Ibrar
- grid.461150.7Farooq Hospital, Lahore, Pakistan
| | - Eatedal Alabdulkreem
- grid.449346.80000 0004 0501 7602Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671 Saudi Arabia
| | - Mohamed K. Nour
- grid.412832.e0000 0000 9137 6644Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Mohammed S. Alqahtani
- grid.412144.60000 0004 1790 7100Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421 Saudi Arabia
| | - Faisal Mohammed Nafie
- grid.449051.d0000 0004 0441 5633Department of Computer Science, College of Science and Humanities at Alghat, Majmaah University, Al-Majmaah, 11952 Saudi Arabia
| | - Abdullah Mohamed
- grid.440865.b0000 0004 0377 3762Research Centre, Future University in Egypt, New Cairo, 11845 Egypt
| | - Gouse Pasha Mohammed
- grid.449553.a0000 0004 0441 5588Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Tim Q. Duong
- grid.240283.f0000 0001 2152 0791Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467 USA
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28
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Haldar D, Kazerooni AF, Arif S, Familiar A, Madhogarhia R, Khalili N, Bagheri S, Anderson H, Shaikh IS, Mahtabfar A, Kim MC, Tu W, Ware J, Vossough A, Davatzikos C, Storm PB, Resnick A, Nabavizadeh A. Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers. Neoplasia 2023; 36:100869. [PMID: 36566592 PMCID: PMC9803939 DOI: 10.1016/j.neo.2022.100869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/21/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. METHODS Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. RESULTS K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes. CONCLUSION In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.
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Affiliation(s)
- Debanjan Haldar
- Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sherjeel Arif
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Rachel Madhogarhia
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Aria Mahtabfar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Neurological Surgery, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Meen Chul Kim
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Wenxin Tu
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jefferey Ware
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Division of Neurological Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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29
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Raunig DL, Pennello GA, Delfino JG, Buckler AJ, Hall TJ, Guimaraes AR, Wang X, Huang EP, Barnhart HX, deSouza N, Obuchowski N. Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap. Acad Radiol 2023; 30:159-182. [PMID: 36464548 PMCID: PMC9825667 DOI: 10.1016/j.acra.2022.10.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 12/02/2022]
Abstract
Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.
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Affiliation(s)
- David L Raunig
- Department of Statistical and Quantitative Sciences, Data Science Institute, Takeda Pharmaceuticals, Cambridge, Massachusetts.
| | - Gene A Pennello
- Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Jana G Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland, Ohio
| | - Erich P Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Huiman X Barnhart
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Nandita deSouza
- Division of Radiotherapy and Imaging, the Insitute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nancy Obuchowski
- Department of Quantitative Health Sciences, Lerner Research Institute Cleveland Clinic Foundation, Cleveland, Ohio
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Yeh MY, Chen HS, Hou P, Kumar VA, Johnson JM, Noll KR, Prabhu SS, Ferguson SD, Schomer DF, Peng HH, Liu HL. Cerebrovascular Reactivity Mapping Using Resting-State Functional MRI in Patients With Gliomas. J Magn Reson Imaging 2022; 56:1863-1871. [PMID: 35396789 DOI: 10.1002/jmri.28194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Recently, a data-driven regression analysis method was developed to utilize the resting-state (rs) blood oxygenation level-dependent signal for cerebrovascular reactivity (CVR) mapping (rs-CVR), which was previously optimized by comparing with the CO2 inhalation-based method in health subjects and patients with neurovascular diseases. PURPOSE To investigate the agreement of rs-CVR and the CVR mapping with breath-hold MRI (bh-CVR) in patients with gliomas. STUDY TYPE Retrospective. POPULATION Twenty-five patients (12 males, 13 females; mean age ± SD, 48 ± 13 years) with gliomas. FIELD STRENGTH/SEQUENCE Dynamic T2*-weighted gradient-echo echo-planar imaging during a breath-hold paradigm and during the rs on a 3-T scanner. ASSESSMENT rs-CVR with various frequency ranges and resting-state fluctuation amplitude (RSFA) were assessed. The agreement between each rs-based CVR measurement and bh-CVR was determined by voxel-wise correlation and Dice coefficient in the whole brain, gray matter, and the lesion region of interest (ROI). STATISTICAL TESTS Voxel-wise Pearson correlation, Dice coefficient, Fisher Z-transformation, repeated-measure analysis of variance and post hoc test with Bonferroni correction, and nonparametric repeated-measure Friedman test and post hoc test with Bonferroni correction were used. Significance was set at P < 0.05. RESULTS Compared with bh-CVR, the highest correlations were found at the frequency bands of 0.04-0.08 Hz and 0.02-0.04 Hz for rs-CVR in both whole brain and the lesion ROI. RSFA had significantly lower correlations than did rs-CVR of 0.02-0.04 Hz and a wider frequency range (0-0.1164 Hz). Significantly higher correlations and Dice coefficient were found in normal tissues than in the lesion ROI for all three methods. DATA CONCLUSION The optimal frequency ranges for rs-CVR are determined by comparing with bh-CVR in patients with gliomas. The rs-CVR method outperformed the RSFA. Significantly higher correlation and Dice coefficient between rs- and bh-CVR were found in normal tissue than in the lesion. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Mei-Yu Yeh
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Henry S Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ping Hou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Vinodh A Kumar
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason M Johnson
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kyle R Noll
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sujit S Prabhu
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sherise D Ferguson
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donald F Schomer
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hsu-Hsia Peng
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Improved generalized ComBat methods for harmonization of radiomic features. Sci Rep 2022; 12:19009. [PMID: 36348002 PMCID: PMC9643436 DOI: 10.1038/s41598-022-23328-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/29/2022] [Indexed: 11/10/2022] Open
Abstract
Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested - GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested - GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.
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Pati S, Baid U, Edwards B, Sheller MJ, Foley P, Reina GA, Thakur S, Sako C, Bilello M, Davatzikos C, Martin J, Shah P, Menze B, Bakas S. The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research. Phys Med Biol 2022; 67:10.1088/1361-6560/ac9449. [PMID: 36137534 PMCID: PMC9592188 DOI: 10.1088/1361-6560/ac9449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/22/2022] [Indexed: 11/11/2022]
Abstract
Objective.De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.Approach.Towards this end, this manuscript describes theFederatedTumorSegmentation (FeTS) tool, in terms of software architecture and functionality.Main results.The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.Significance.Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | - Siddhesh Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Bakas S, Sako C, Akbari H, Bilello M, Sotiras A, Shukla G, Rudie JD, Santamaría NF, Kazerooni AF, Pati S, Rathore S, Mamourian E, Ha SM, Parker W, Doshi J, Baid U, Bergman M, Binder ZA, Verma R, Lustig RA, Desai AS, Bagley SJ, Mourelatos Z, Morrissette J, Watt CD, Brem S, Wolf RL, Melhem ER, Nasrallah MP, Mohan S, O'Rourke DM, Davatzikos C. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Sci Data 2022; 9:453. [PMID: 35906241 PMCID: PMC9338035 DOI: 10.1038/s41597-022-01560-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/12/2022] [Indexed: 02/05/2023] Open
Abstract
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
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Affiliation(s)
- Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Natali Flores Santamaría
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - William Parker
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arati S Desai
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zissimos Mourelatos
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Watt
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronald L Wolf
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elias R Melhem
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Chitalia R, Pati S, Bhalerao M, Thakur SP, Jahani N, Belenky V, McDonald ES, Gibbs J, Newitt DC, Hylton NM, Kontos D, Bakas S. Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1. Sci Data 2022; 9:440. [PMID: 35871247 PMCID: PMC9308769 DOI: 10.1038/s41597-022-01555-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
Abstract
Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
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Affiliation(s)
- Rhea Chitalia
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Megh Bhalerao
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Siddhesh Pravin Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nariman Jahani
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vivian Belenky
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth S McDonald
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jessica Gibbs
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - David C Newitt
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - Nola M Hylton
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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The New Era of Salivaomics in Dentistry: Frontiers and Facts in the Early Diagnosis and Prevention of Oral Diseases and Cancer. Metabolites 2022; 12:metabo12070638. [PMID: 35888762 PMCID: PMC9319392 DOI: 10.3390/metabo12070638] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/27/2022] [Accepted: 07/06/2022] [Indexed: 12/22/2022] Open
Abstract
Nowadays, with the development of new and highly sensitive, blood is not the only medium of choice for the diagnosis of several diseases and pathological conditions. Saliva is now considered a safe and non-invasive sample to study oral and systemic diseases, showing great diagnostic potential. According to several recent studies, saliva has emerged as an emerging biofluid for the early diagnosis of several diseases, indicated as a mirror of oral and systemic health and a valuable source of clinically relevant information. Indeed, several studies have observed that saliva is useful for detecting and diagnosing malignant tumours, human immunodeficiency virus, heart disease, and autoimmune diseases. The growing realisation that saliva is an inexhaustible source of information has led to the coining of the term ‘Salivaomics’, which includes five “omics” in connection with the main constituents of saliva: genome and epigenome, transcriptomics, metabolomics, lipidomics, proteomics and microbiota. All those may be changed by disease state, so they offer significant advantages in the early diagnosis and prognosis of oral diseases. The aim of the present review isto update and highlight the new frontiers of salivaomics in diagnosing and managing oral disorders, such as periodontitis, premalignant disorders, and oral squamous cell carcinoma (OSCC).
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Ismail M, Prasanna P, Bera K, Statsevych V, Hill V, Singh G, Partovi S, Beig N, McGarry S, Laviolette P, Ahluwalia M, Madabhushi A, Tiwari P. Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1764-1777. [PMID: 35108202 PMCID: PMC9575333 DOI: 10.1109/tmi.2022.3148780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.
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Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features. Sci Data 2022; 9:338. [PMID: 35701399 PMCID: PMC9198015 DOI: 10.1038/s41597-022-01415-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/24/2022] [Indexed: 01/26/2023] Open
Abstract
Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).
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Thakran S, Cohen E, Jahani N, Weinstein SP, Pantalone L, Hylton N, Newitt D, DeMichele A, Davatzikos C, Kontos D. Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial. Transl Oncol 2022; 20:101411. [PMID: 35395604 PMCID: PMC8990167 DOI: 10.1016/j.tranon.2022.101411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/09/2022] [Accepted: 03/27/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Image registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer. METHODS 130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots. RESULTS A baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV2)) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model. CONCLUSION Incorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods.
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Affiliation(s)
- Snekha Thakran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Eric Cohen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Nariman Jahani
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Susan P Weinstein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94115 United States
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94115 United States
| | - Angela DeMichele
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States.
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Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma. Sci Rep 2022; 12:8784. [PMID: 35610333 PMCID: PMC9130299 DOI: 10.1038/s41598-022-12699-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 05/06/2022] [Indexed: 02/05/2023] Open
Abstract
Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.
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Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects. Sci Rep 2022; 12:4493. [PMID: 35296726 PMCID: PMC8927332 DOI: 10.1038/s41598-022-08412-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 03/07/2022] [Indexed: 01/20/2023] Open
Abstract
Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses.
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Morrison G, Buckley J, Ostrow D, Varghese B, Cen SY, Werbin J, Ericson N, Cunha A, Lu YT, George T, Smith J, Quinn D, Duddalwar V, Triche T, Goldkorn A. Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis. Int J Mol Sci 2022; 23:2571. [PMID: 35269713 PMCID: PMC8910093 DOI: 10.3390/ijms23052571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 11/16/2022] Open
Abstract
Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.
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Affiliation(s)
- Gareth Morrison
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Jonathan Buckley
- Department of Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Dejerianne Ostrow
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
| | - Steven Y. Cen
- Departments of Radiology and Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA;
| | - Jeffrey Werbin
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Nolan Ericson
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Alexander Cunha
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Yi-Tsung Lu
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Thaddeus George
- RareCyte, Inc., Seattle, WA 98121, USA; (J.W.); (N.E.); (T.G.)
| | - Jeffrey Smith
- Clinical Sequencing Division, Thermo Fisher Scientific, San Francisco, CA 94080, USA;
| | - David Quinn
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
| | - Vinay Duddalwar
- Departments of Radiology and Urology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA;
| | - Timothy Triche
- Center for Personalized Medicine, Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA; (D.O.); (T.T.)
| | - Amir Goldkorn
- Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; (G.M.); (A.C.); (Y.-T.L.); (D.Q.)
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Booth TC, Grzeda M, Chelliah A, Roman A, Al Busaidi A, Dragos C, Shuaib H, Luis A, Mirchandani A, Alparslan B, Mansoor N, Lavrador J, Vergani F, Ashkan K, Modat M, Ourselin S. Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies. Front Oncol 2022; 12:799662. [PMID: 35174084 PMCID: PMC8842649 DOI: 10.3389/fonc.2022.799662] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/03/2022] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies. METHODS Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018-01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965). RESULTS Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC. CONCLUSION ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Mariusz Grzeda
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Andrei Roman
- Department of Radiology, Guy’s & St. Thomas’ National Health Service Foundation Trust, London, United Kingdom
- Department of Radiology, The Oncology Institute “Prof. Dr. Ion Chiricuţă” Cluj-Napoca, Cluj-Napoca, Romania
| | - Ayisha Al Busaidi
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Carmen Dragos
- Department of Radiology, Buckinghamshire Healthcare National Health Service Trust, Amersham, United Kingdom
| | - Haris Shuaib
- Department of Medical Physics, Guy’s & St. Thomas’ National Health Service Foundation Trust, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Aysha Luis
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Ayesha Mirchandani
- Department of Radiology, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, United Kingdom
| | - Burcu Alparslan
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
- Department of Radiology, Kocaeli University, İzmit, Turkey
| | - Nina Mansoor
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Jose Lavrador
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Francesco Vergani
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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Dong J, Li S, Li L, Liang S, Zhang B, Meng Y, Zhang X, Zhang Y, Zhao S. Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions. Br J Radiol 2022; 95:20201302. [PMID: 34767476 PMCID: PMC8722235 DOI: 10.1259/bjr.20201302] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of a radiomics model based on multiregional and multiparametric MRI to classify paediatric posterior fossa tumours (PPFTs), explore the contribution of different MR sequences and tumour subregions in tumour classification, and examine whether contrast-enhanced T1 weighted (T1C) images have irreplaceable added value. METHODS This retrospective study of 136 PPFTs extracted 11,958 multiregional (enhanced, non-enhanced, and total tumour) features from multiparametric MRI (T1- and T2 weighted, T1C, fluid-attenuated inversion recovery, and diffusion-weighted images). These features were subjected to fast correlation-based feature selection and classified by a support vector machine based on different tasks. Diagnostic performances of multiregional and multiparametric MRI features, different sequences, and different tumoral regions were evaluated using multiclass and one-vs-rest strategies. RESULTS The established model achieved an overall area under the curve (AUC) of 0.977 in the validation cohort. The performance of PPFTs significantly improved after replacing T1C with apparent diffusion coefficient maps added into the plain scan sequences (AUC from 0.812 to 0.917). When oedema features were added to contrast-enhancing tumour volume, the performance did not significantly improve. CONCLUSION The radiomics model built by multiregional and multiparametric MRI features allows for the excellent distinction of different PPFTs and provides valuable references for the rational adoption of MR sequences. ADVANCES IN KNOWLEDGE This study emphasized that T1C has limited added value in predicting PPFTs and should be cautiously adopted. Selecting optimal MR sequences may help guide clinicians to better allocate acquisition sequences and reduce medical costs.
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Affiliation(s)
- Jie Dong
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
| | - Suxiao Li
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
| | - Lei Li
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
| | | | - Bin Zhang
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
| | - Yun Meng
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Xiaofang Zhang
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
| | - Yong Zhang
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Shujun Zhao
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China
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Thakur SP, Pati S, Panchumarthy R, Karkada D, Wu J, Kurtaev D, Sako C, Shah P, Bakas S. Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2022; 12962:151-167. [PMID: 36331281 PMCID: PMC9627678 DOI: 10.1007/978-3-031-08999-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Brain extraction is an indispensable step in neuro-imaging with a direct impact on downstream analyses. Most such methods have been developed for non-pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited, primarily due to these methods suffering from i) high computational cost, and ii) specific hardware requirements, e.g., DL acceleration cards. In this study, we explore the potential of mathematical optimizations, towards making DL methods amenable to application in low resource environments. We focus on both the qualitative and quantitative evaluation of such optimizations on an existing DL brain extraction method, designed for pathologically-affected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, through-put, and reduction in memory usage, while the segmentation performance of the initial and the optimized models remains stable, i.e., as quantified by both the Dice Similarity Coefficient and the Hausdorff Distance. These findings support post-training optimizations as a promising approach for enabling the execution of advanced DL methodologies on plain commercial-grade CPUs, and hence contributing to their translation in limited- and low- resource clinical environments.
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Affiliation(s)
- Siddhesh P Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ravi Panchumarthy
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Deepthi Karkada
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Junwen Wu
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Dmitry Kurtaev
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prashant Shah
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Madhogarhia R, Haldar D, Bagheri S, Familiar A, Anderson H, Arif S, Vossough A, Storm P, Resnick A, Davatzikos C, Fathi Kazerooni A, Nabavizadeh A. Radiomics and radiogenomics in pediatric neuro-oncology: A review. Neurooncol Adv 2022; 4:vdac083. [PMID: 35795472 PMCID: PMC9252112 DOI: 10.1093/noajnl/vdac083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.
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Affiliation(s)
- Rachel Madhogarhia
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Debanjan Haldar
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sherjeel Arif
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Phillip Storm
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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47
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Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Conant EF, Scott CG, Winham SJ, Kerlikowske K, Vachon C, Maidment ADA, Kontos D. Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation. Cancers (Basel) 2021; 13:5497. [PMID: 34771660 PMCID: PMC8582675 DOI: 10.3390/cancers13215497] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/16/2021] [Accepted: 10/27/2021] [Indexed: 11/17/2022] Open
Abstract
Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns-a woman's left and right breasts. From 341 features, we identified "robust" features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.
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Affiliation(s)
- Raymond J. Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Eric A. Cohen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Omid Haji Maghsoudi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Aimilia Gastounioti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Meng-Kang Hsieh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Emily F. Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Christopher G. Scott
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA; (C.G.S.); (S.J.W.); (C.V.)
| | - Stacey J. Winham
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA; (C.G.S.); (S.J.W.); (C.V.)
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology/Biostatistics, Women’s Health Clinical Research Center, UCSF, San Francisco, CA 94143, USA;
| | - Celine Vachon
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA; (C.G.S.); (S.J.W.); (C.V.)
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (E.A.C.); (O.H.M.); (A.G.); (L.P.); (M.-K.H.); (E.F.C.); (A.D.A.M.); (D.K.)
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Khanna O, Fathi Kazerooni A, Farrell CJ, Baldassari MP, Alexander TD, Karsy M, Greenberger BA, Garcia JA, Sako C, Evans JJ, Judy KD, Andrews DW, Flanders AE, Sharan AD, Dicker AP, Shi W, Davatzikos C. Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas. Neurosurgery 2021; 89:928-936. [PMID: 34460921 DOI: 10.1093/neuros/nyab307] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Although World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.
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Affiliation(s)
- Omaditya Khanna
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christopher J Farrell
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael P Baldassari
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Tyler D Alexander
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael Karsy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Benjamin A Greenberger
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jose A Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James J Evans
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Kevin D Judy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - David W Andrews
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam E Flanders
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Ashwini D Sharan
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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49
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Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI. Cancers (Basel) 2021; 13:cancers13205047. [PMID: 34680199 PMCID: PMC8533879 DOI: 10.3390/cancers13205047] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 01/06/2023] Open
Abstract
Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
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50
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Lei M, Varghese B, Hwang D, Cen S, Lei X, Desai B, Azadikhah A, Oberai A, Duddalwar V. Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis. J Digit Imaging 2021; 34:1156-1170. [PMID: 34545475 PMCID: PMC8554949 DOI: 10.1007/s10278-021-00506-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/07/2021] [Accepted: 08/17/2021] [Indexed: 01/06/2023] Open
Abstract
The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction.
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Affiliation(s)
- Mingxi Lei
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089 USA
| | - Bino Varghese
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Darryl Hwang
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Steven Cen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089 USA
| | - Xiaomeng Lei
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Bhushan Desai
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Afshin Azadikhah
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Assad Oberai
- Department of Aerospace and Mechanical Engineering, University of Southern California, 854 Downey Way, Los Angeles, CA 90089 USA
| | - Vinay Duddalwar
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
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