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Garcia-Ruiz A, Pons-Escoda A, Grussu F, Naval-Baudin P, Monreal-Aguero C, Hermann G, Karunamuni R, Ligero M, Lopez-Rueda A, Oleaga L, Berbís MÁ, Cabrera-Zubizarreta A, Martin-Noguerol T, Luna A, Seibert TM, Majos C, Perez-Lopez R. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Rep Med 2024; 5:101464. [PMID: 38471504 PMCID: PMC10983037 DOI: 10.1016/j.xcrm.2024.101464] [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: 08/30/2023] [Revised: 11/16/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.
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Affiliation(s)
- Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Albert Pons-Escoda
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain
| | | | - Gretchen Hermann
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Roshan Karunamuni
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | | | - Laura Oleaga
- Radiology Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - M Álvaro Berbís
- Radiology Department, HT Medica, Hospital San Juan de Dios, 14012 Cordoba, Spain
| | | | | | - Antonio Luna
- Radiology Department, HT Medica, 23008 Jaen, Spain
| | - Tyler M Seibert
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Radiology Department, University of California, San Diego, La Jolla, CA 92093, USA; Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Carlos Majos
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
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2
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Garaba A, Aslam N, Ponzio F, Panciani PP, Brinjikji W, Fontanella M, De Maria L. Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis. Front Oncol 2024; 14:1291861. [PMID: 38420015 PMCID: PMC10899458 DOI: 10.3389/fonc.2024.1291861] [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: 09/10/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Background and objective Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms. Methods A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test. Results The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 - 0.91) and 87% (95% CI = 0.83 - 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%). Conclusion The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.
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Affiliation(s)
- Alexandru Garaba
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Nummra Aslam
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Francesco Ponzio
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino, Italy
| | - Pier Paolo Panciani
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Waleed Brinjikji
- Department of Neurosurgery and Interventional Neuroradiology, Mayo Clinic, Rochester, MN, United States
| | - Marco Fontanella
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Lucio De Maria
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
- Department of Clinical Neuroscience, Geneva University Hospitals (HUG), Geneva, Switzerland
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Hill HA, Jain P, Ok CY, Sasaki K, Chen H, Wang ML, Chen K. Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma. CANCER RESEARCH COMMUNICATIONS 2023; 3:1435-1446. [PMID: 37538987 PMCID: PMC10395375 DOI: 10.1158/2767-9764.crc-23-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 08/05/2023]
Abstract
Patients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curated an extensive database of 862 patients diagnosed between 2014 and 2022. A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathologic, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83). In addition, we utilized the gradient-boosted framework as a robust feature selection method for multivariate logistic and survival modeling. The best ML models incorporated features from clinical and genomic data types highlighting the need for correlative molecular studies in precision oncology. As proof of concept, we launched our most accurate and practical models using an application interface, which has potential for clinical implementation. We designated the 20-feature ML model-based index the "integrative MIPI" or iMIPI and a similar 10-feature ML index the "integrative simplified MIPI" or iMIPI-s. The top 10 baseline prognostic features represented in the iMIPI-s are: lactase dehydrogenase (LDH), Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin levels, the total number of observed somatic mutations, TP53 mutational status, Eastern Cooperative Oncology Group performance level, beta-2 microglobulin, and morphology. Our findings emphasize that prognostic applications and indices should include molecular features, especially TP53 mutational status. This work demonstrates the clinical utility of complex ML models and provides further evidence for existing prognostic markers in MCL. Significance Our model is the first to integrate a dynamic algorithm with multiple clinical and molecular features, allowing for accurate predictions of MCL disease outcomes in a large patient cohort.
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Affiliation(s)
- Holly A. Hill
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas
| | - Preetesh Jain
- Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chi Young Ok
- Department of Hematopathology, Division of Pathology-Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Koji Sasaki
- Department of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Han Chen
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Michael L. Wang
- Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
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4
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Priya S, Ward C, Bathla G. Letter to editor regarding article "fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis". J Neuroradiol 2023; 50:40-41. [PMID: 36610935 DOI: 10.1016/j.neurad.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 12/25/2022] [Indexed: 01/07/2023]
Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA.
| | - Caitlin Ward
- Division of Biostatistics, School of Public Health, University of Minnesota, USA
| | - Girish Bathla
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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5
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Nenning KH, Gesperger J, Furtner J, Nemc A, Roetzer-Pejrimovsky T, Choi SW, Mitter C, Leber SL, Hofmanninger J, Klughammer J, Ergüner B, Bauer M, Brada M, Chong K, Brandner-Kokalj T, Freyschlag CF, Grams A, Haybaeck J, Hoenigschnabl S, Hoffermann M, Iglseder S, Kiesel B, Kitzwoegerer M, Kleindienst W, Marhold F, Moser P, Oberndorfer S, Pinggera D, Scheichel F, Sherif C, Stockhammer G, Stultschnig M, Thomé C, Trenkler J, Urbanic-Purkart T, Weis S, Widhalm G, Wuertz F, Preusser M, Baumann B, Simonitsch-Klupp I, Nam DH, Bock C, Langs G, Woehrer A. Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma. Neurooncol Adv 2023; 5:vdad136. [PMID: 38024240 PMCID: PMC10676053 DOI: 10.1093/noajnl/vdad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
Background The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification. Methods In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines. Results The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients. Conclusions The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.
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Affiliation(s)
- Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA
| | - Johanna Gesperger
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
| | - Amelie Nemc
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Seung-Won Choi
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Christian Mitter
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stefan L Leber
- Division of Neuroradiology, Vascular, and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Johannes Hofmanninger
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Biochemistry, Gene Center, Ludwig-Maximilians-University, München, Germany
| | - Bekir Ergüner
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Marlies Bauer
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Martina Brada
- Department of Pathology, Klinik Landstraße, Vienna, Austria
| | - Kyuha Chong
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | | | - Astrid Grams
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Haybaeck
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
- Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, Diagnostic and Research, Graz, Austria
| | | | - Markus Hoffermann
- Department of Neurosurgery, State Hospital Feldkirch, Feldkirch, Austria
| | - Sarah Iglseder
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Melitta Kitzwoegerer
- Department of Pathology, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Waltraud Kleindienst
- Department of Neurology, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Patrizia Moser
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
- Department of Pathology, Innpath, Tirolkliniken, Innsbruck, Austria
| | - Stefan Oberndorfer
- Department of Neurology, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Daniel Pinggera
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Scheichel
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | | | | | - Claudius Thomé
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Trenkler
- Institute of Neuroradiology, Kepler University Hospital, NeuromedCampus, Johannes Kepler University of Linz, Linz, Austria
| | - Tadeja Urbanic-Purkart
- Department of Neurology, Medical University of Graz, Graz, Austria
- Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Serge Weis
- Division of Neuropathology, Kepler University Hospital, NeuromedCampus, Johannes Kepler University, Linz, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Franz Wuertz
- Institute of Pathology, State Hospital Klagenfurt, Klagenfurt, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine 1, Medical University of Vienna, Vienna, Austria
| | - Bernhard Baumann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Do-Hyun Nam
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
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Kotsyfakis S, Iliaki-Giannakoudaki E, Anagnostopoulos A, Papadokostaki E, Giannakoudakis K, Goumenakis M, Kotsyfakis M. The application of machine learning to imaging in hematological oncology: A scoping review. Front Oncol 2022; 12:1080988. [PMID: 36605438 PMCID: PMC9808781 DOI: 10.3389/fonc.2022.1080988] [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: 10/26/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Background Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps. Methods The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies. Results Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation. Conclusion To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.
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Affiliation(s)
| | | | | | | | | | | | - Michail Kotsyfakis
- Biology Center of the Czech Academy of Sciences, Budweis (Ceske Budejovice), Czechia,*Correspondence: Michail Kotsyfakis,
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Cao L, Zhang M, Zhang Y, Ji B, Wang X, Wang X. Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review). Oncol Rep 2022; 49:20. [PMID: 36484403 PMCID: PMC9773014 DOI: 10.3892/or.2022.8457] [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: 06/24/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.
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Affiliation(s)
- Luming Cao
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Ying Zhang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Bin Ji
- Department of Nuclear Medicine, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xuemei Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xueju Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China,Correspondence to: Dr Xueju Wang, Department of Pathology, China-Japan Union Hospital, Jilin University, 126 Xiantai Street, Changchun, Jilin 130033, P.R. China, E-mail:
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8
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Tang Y, Shi Y, Wang L, Qian ZT, Fan YW, Wu HM, Li X. Preliminary clinical application of multimodal imaging combined with frameless robotic stereotactic biopsy in the diagnosis of primary central nervous system lymphoma. Heliyon 2022; 8:e12162. [DOI: 10.1016/j.heliyon.2022.e12162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 11/06/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
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Zhu M, Li S, Kuang Y, Hill VB, Heimberger AB, Zhai L, Zhai S. Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Front Oncol 2022; 12:924245. [PMID: 35982952 PMCID: PMC9379255 DOI: 10.3389/fonc.2022.924245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022] Open
Abstract
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.
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Affiliation(s)
- Ming Zhu
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Sijia Li
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Yu Kuang
- Medical Physics Program, Department of Health Physics, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Virginia B. Hill
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Amy B. Heimberger
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Lijie Zhai
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
| | - Shengjie Zhai
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
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A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022; 14:cancers14112731. [PMID: 35681711 PMCID: PMC9179305 DOI: 10.3390/cancers14112731] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx. Abstract Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.
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Ariza-Colpas PP, Vicario E, Oviedo-Carrascal AI, Butt Aziz S, Piñeres-Melo MA, Quintero-Linero A, Patara F. Human Activity Recognition Data Analysis: History, Evolutions, and New Trends. SENSORS 2022; 22:s22093401. [PMID: 35591091 PMCID: PMC9103712 DOI: 10.3390/s22093401] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 01/23/2023]
Abstract
The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.
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Affiliation(s)
- Paola Patricia Ariza-Colpas
- Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia
- Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medellín 050031, Colombia;
- Correspondence:
| | - Enrico Vicario
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy; (E.V.); (F.P.)
| | - Ana Isabel Oviedo-Carrascal
- Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medellín 050031, Colombia;
| | - Shariq Butt Aziz
- Department of Computer Science and IT, University of Lahore, Lahore 44000, Pakistan;
| | | | | | - Fulvio Patara
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy; (E.V.); (F.P.)
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep 2021; 11:10478. [PMID: 34006893 PMCID: PMC8131619 DOI: 10.1038/s41598-021-90032-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 05/05/2021] [Indexed: 01/19/2023] Open
Abstract
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
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Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
| | - Yanan Liu
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Nam H Le
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | | | - Varun Monga
- Department of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Honghai Zhang
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Milan Sonka
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Girish Bathla
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
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Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison-Cardiac MRI Radiomics in Pulmonary Hypertension. J Clin Med 2021; 10:jcm10091921. [PMID: 33925262 PMCID: PMC8125238 DOI: 10.3390/jcm10091921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 12/31/2022] Open
Abstract
The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions.
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