1
|
Kersch CN, Kim M, Stoller J, Barajas RF, Park JE. Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity. AJNR Am J Neuroradiol 2024; 45:537-548. [PMID: 38548303 DOI: 10.3174/ajnr.a8148] [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: 07/28/2023] [Accepted: 11/09/2023] [Indexed: 04/12/2024]
Abstract
An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.
Collapse
Affiliation(s)
- Cymon N Kersch
- From the Department of Radiation Medicine (C.N.K.), Oregon Health and Science University, Portland, Oregon
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jared Stoller
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ramon F Barajas
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
- Knight Cancer Institute (R.F.B.), Oregon Health and Science University, Portland, Oregon
- Advanced Imaging Research Center (R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| |
Collapse
|
2
|
Wang L, Wang H, D’Angelo F, Curtin L, Sereduk CP, Leon GD, Singleton KW, Urcuyo J, Hawkins-Daarud A, Jackson PR, Krishna C, Zimmerman RS, Patra DP, Bendok BR, Smith KA, Nakaji P, Donev K, Baxter LC, Mrugała MM, Ceccarelli M, Iavarone A, Swanson KR, Tran NL, Hu LS, Li J. Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm. PLoS One 2024; 19:e0299267. [PMID: 38568950 PMCID: PMC10990246 DOI: 10.1371/journal.pone.0299267] [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: 07/31/2023] [Accepted: 02/06/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. METHODS We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. RESULTS WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. CONCLUSIONS This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
Collapse
Affiliation(s)
- Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Hairong Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Fulvio D’Angelo
- Institute for Cancer Genetics, Columbia University Medical Center, New York City, New York, United States of America
| | - Lee Curtin
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Christopher P. Sereduk
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Javier Urcuyo
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute—St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, United States of America
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute—St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Leslie C. Baxter
- Department of Neuropsychology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugała
- Department of Neuro-Oncology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Naples, Italy
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York City, New York, United States of America
| | - Kristin R. Swanson
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| |
Collapse
|
3
|
Wang H, Argenziano MG, Yoon H, Boyett D, Save A, Petridis P, Savage W, Jackson P, Hawkins-Daarud A, Tran N, Hu L, Al Dalahmah O, Bruce JN, Grinband J, Swanson KR, Canoll P, Li J. Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma. RESEARCH SQUARE 2024:rs.3.rs-3891425. [PMID: 38585856 PMCID: PMC10996806 DOI: 10.21203/rs.3.rs-3891425/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.
Collapse
Affiliation(s)
- Hairong Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael G Argenziano
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Hyunsoo Yoon
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Deborah Boyett
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Akshay Save
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Petros Petridis
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
- Department of Psychiatry, New York University, New York, NY, USA
| | - William Savage
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Pamela Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - Nhan Tran
- Department of Cancer Biology, Mayo Clinic, Phoenix, AZ, USA
| | - Leland Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Osama Al Dalahmah
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jeffrey N. Bruce
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Jack Grinband
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - Peter Canoll
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| |
Collapse
|
4
|
Liang Q, Jing H, Shao Y, Wang Y, Zhang H. Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas. Clin Neuroradiol 2024; 34:33-43. [PMID: 38277059 DOI: 10.1007/s00062-023-01375-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: 10/07/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.
Collapse
Affiliation(s)
- Qian Liang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Hui Jing
- Department of MRI, The Sixth Hospital, Shanxi Medical University, 030008, Taiyuan, Shanxi Province, China
| | - Yingbo Shao
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Yinhua Wang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
| |
Collapse
|
5
|
Lee J, Chen MM, Liu HL, Ucisik FE, Wintermark M, Kumar VA. MR Perfusion Imaging for Gliomas. Magn Reson Imaging Clin N Am 2024; 32:73-83. [PMID: 38007284 DOI: 10.1016/j.mric.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.
Collapse
Affiliation(s)
- Jina Lee
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Melissa M Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Vinodh A Kumar
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
| |
Collapse
|
6
|
Lan L, Feng K, Wu Y, Zhang W, Wei L, Che H, Xue L, Gao Y, Tao J, Qian S, Cao W, Zhang J, Wang C, Tian M. Phenomic Imaging. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:597-612. [PMID: 38223684 PMCID: PMC10781914 DOI: 10.1007/s43657-023-00128-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 01/16/2024]
Abstract
Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.
Collapse
Affiliation(s)
- Lizhen Lan
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Kai Feng
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Yudan Wu
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Wenbo Zhang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ling Wei
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Huiting Che
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Yidan Gao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ji Tao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Shufang Qian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Wenzhao Cao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Fudan University, Shanghai, 200040 China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Mei Tian
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| |
Collapse
|
7
|
Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen GC, Bahar R, Gordem A, Haider MA, Subramanian H, Brim W, Ikuta I, Omuro A, Conte GM, Marquez-Nostra BV, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni AF, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. AJNR Am J Neuroradiol 2023; 44:1126-1134. [PMID: 37770204 PMCID: PMC10549943 DOI: 10.3174/ajnr.a8000] [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: 02/27/2023] [Accepted: 08/01/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor. PURPOSE We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging. DATA SOURCES Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science. STUDY SELECTION Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria. DATA ANALYSIS We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines. DATA SYNTHESIS Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles. LIMITATIONS The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias. CONCLUSIONS While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.
Collapse
Affiliation(s)
- Jan Lost
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Tej Verma
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Leon Jekel
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Marc von Reppert
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Niklas Tillmanns
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sara Merkaj
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Gabriel Cassinelli Petersen
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ryan Bahar
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ayyüce Gordem
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Muhammad A Haider
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Harry Subramanian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Waverly Brim
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ichiro Ikuta
- Department of Radiology (I.I.), Mayo Clinic Arizona, Phoenix, Arizona
| | - Antonio Omuro
- Department of Neurology and Yale Cancer Center (A.O.), Yale School of Medicine, New Haven, Connecticut
| | - Gian Marco Conte
- Department of Radiology (G.M.C.), Mayo Clinic, Rochester, Minesotta
| | - Bernadette V Marquez-Nostra
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Arman Avesta
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | | | - Ali Nabavizadeh
- Department of Radiology (A.N.), Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anahita Fathi Kazerooni
- Department of Neurosurgery (A.F.K.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Division of Neurosurgery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Center for Data-Driven Discovery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sanjay Aneja
- Department of Therapeutic Radiology (S.A), Yale School of Medicine, New Haven, Connecticut
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Richards Medical Research Laboratories (S.B.), Philadelphia, Pennsylvania
- Department of Radiology (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - MingDe Lin
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Visage Imaging Inc (K.B., M.L.), San Diego, California
| | - Michael Sabel
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Mariam Aboian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| |
Collapse
|
8
|
Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers (Basel) 2023; 15:3845. [PMID: 37568660 PMCID: PMC10417709 DOI: 10.3390/cancers15153845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.
Collapse
Affiliation(s)
- Mehnaz Tabassum
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Eric Suero Molina
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Department of Neurosurgery, University Hospital of Münster, 48149 Münster, Germany
| | - Elizabeth Pan
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Sidong Liu
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| |
Collapse
|
9
|
Baheti B, Pati S, Menze B, Bakas S. Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2023; 13769:68-79. [PMID: 37928819 PMCID: PMC10623403 DOI: 10.1007/978-3-031-33842-7_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Convolutional neural networks (CNNs) have shown promising performance in various 2D computer vision tasks due to availability of large amounts of 2D training data. Contrarily, medical imaging deals with 3D data and usually lacks the equivalent extent and diversity of data, for developing AI models. Transfer learning provides the means to use models trained for one application as a starting point to another application. In this work, we leverage 2D pre-trained models as a starting point in 3D medical applications by exploring the concept of Axial-Coronal-Sagittal (ACS) convolutions. We have incorporated ACS as an alternative of native 3D convolutions in the Generally Nuanced Deep Learning Framework (GaNDLF), providing various well-established and state-of-the-art network architectures with the availability of pre-trained encoders from 2D data. Results of our experimental evaluation on 3D MRI data of brain tumor patients for i) tumor segmentation and ii) radiogenomic classification, show model size reduction by ~22% and improvement in validation accuracy by ~33%. Our findings support the advantage of ACS convolutions in pre-trained 2D CNNs over 3D CNN without pre-training, for 3D segmentation and classification tasks, democratizing existing models trained in datasets of unprecedented size and showing promise in the field of healthcare.
Collapse
Affiliation(s)
- Bhakti Baheti
- 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
| | - 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
| | - 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
| |
Collapse
|
10
|
Wang R, Chaudhari P, Davatzikos C. Editorial for "Non-Invasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-Institutional Study". J Magn Reson Imaging 2023; 57:897-898. [PMID: 35903917 PMCID: PMC10077882 DOI: 10.1002/jmri.28377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Rongguang Wang
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pratik Chaudhari
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
11
|
Forecasting Molecular Features in IDH-Wildtype Gliomas: The State of the Art of Radiomics Applied to Neurosurgery. Cancers (Basel) 2023; 15:cancers15030940. [PMID: 36765898 PMCID: PMC9913449 DOI: 10.3390/cancers15030940] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, marks a step forward the future diagnostic approach to these neoplasms. Alongside this, radiomics has experienced rapid evolution over the last several years, allowing us to correlate tumor imaging heterogeneity with a wide range of tumor molecular and subcellular features. Radiomics is a translational field focused on decoding conventional imaging data to extrapolate the molecular and prognostic features of tumors such as gliomas. We herein analyze the state-of-the-art of radiomics applied to glioblastoma, with the goal to estimate its current clinical impact and potential perspectives in relation to well-rounded patient management, including the end-of-life stage. METHODS A literature review was performed on the PubMed, MEDLINE and Scopus databases using the following search items: "radiomics and glioma", "radiomics and glioblastoma", "radiomics and glioma and IDH", "radiomics and glioma and TERT promoter", "radiomics and glioma and EGFR", "radiomics and glioma and chromosome". RESULTS A total of 719 articles were screened. Further quantitative and qualitative analysis allowed us to finally include 11 papers. This analysis shows that radiomics is rapidly evolving towards a reliable tool. CONCLUSIONS Further studies are necessary to adjust radiomics' potential to the newest molecular requirements pointed out by the 2021 WHO classification of CNS tumors. At a glance, its application in the clinical routine could be beneficial to achieve a timely diagnosis, especially for those patients not eligible for surgery and/or adjuvant therapies but still deserving palliative and supportive care.
Collapse
|
12
|
Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence. Eur Radiol 2022; 33:3332-3342. [PMID: 36576544 DOI: 10.1007/s00330-022-09365-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/16/2022] [Accepted: 12/05/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. METHODS A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). RESULTS Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. CONCLUSION The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. KEY POINTS • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.
Collapse
|
13
|
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.
Collapse
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:
| |
Collapse
|
14
|
Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis. Clin Neurol Neurosurg 2022; 222:107478. [DOI: 10.1016/j.clineuro.2022.107478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 07/31/2022] [Accepted: 09/27/2022] [Indexed: 11/23/2022]
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Dajani S, Hill VB, Kalapurakal JA, Horbinski CM, Nesbit EG, Sachdev S, Yalamanchili A, Thomas TO. Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy. J Clin Med 2022; 11:jcm11195961. [PMID: 36233828 PMCID: PMC9572863 DOI: 10.3390/jcm11195961] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 12/03/2022] Open
Abstract
Glioblastoma (GBM) continues to be one of the most lethal malignancies and is almost always fatal. In this review article, the role of radiation therapy, systemic therapy, as well as the molecular basis of classifying GBM is described. Technological advances in the treatment of GBM are outlined as well as the diagnostic imaging characteristics of this tumor. In addition, factors that affect prognosis such as differentiating progression from treatment effect is discussed. The role of MRI guided radiation therapy and how this technology may provide a mechanism to improve the care of patients with this disease are described.
Collapse
|
17
|
Sansone G, Vivori N, Vivori C, Di Stefano AL, Picca A. Basic premises: searching for new targets and strategies in diffuse gliomas. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
Liu D, Chen J, Ge H, Hu X, Yang K, Liu Y, Hu G, Luo B, Yan Z, Song K, Xiao C, Zou Y, Zhang W, Liu H. Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features. Front Oncol 2022; 12:848846. [PMID: 35965511 PMCID: PMC9366472 DOI: 10.3389/fonc.2022.848846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/28/2022] [Indexed: 12/14/2022] Open
Abstract
Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.
Collapse
Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Bei Luo
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhen Yan
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Song
- Department of Pathology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yuanjie Zou
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenbin Zhang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Wenbin Zhang, ; Hongyi Liu,
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Wenbin Zhang, ; Hongyi Liu,
| |
Collapse
|
20
|
Liu D, Chen J, Ge H, Yan Z, Luo B, Hu X, Yang K, Liu Y, Liu H, Zhang W. Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur Radiol 2022; 33:209-220. [PMID: 35881182 DOI: 10.1007/s00330-022-09012-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The tumor microenvironment and immune cell infiltration (ICI) associated with glioblastoma (GBM) play a vital role in cancer development, progression, and prognosis. This study aimed to establish an ICI-related prognostic biomarker and explore the associations between ICI signatures and radiomic features in patients with GBM. METHODS The gene expression and survival data of patients with GBM were obtained from three databases. Based on the ICI pattern, an individualized ICI score for each GBM patient was developed in the discovery set (n = 400) and independently verified in the validation set (n = 374). A total of 5915 radiomic features were extracted from the intratumoral and peritumoral regions. Recursive feature elimination and support vector machine methods were performed to select the key features and generate a model predictive of low- or high- ICI scores. The prognostic value of the identified radio genomic model was examined in an independent dataset (n = 149) using imaging and survival data. RESULTS We found that higher ICI scores often indicated worse patient prognosis (multivariable hazard ratio: 0.48 and 0.63 in discovery and validation set, respectively) and higher expression levels of immune checkpoint-related genes. A model that combined 11 radiomic features could well distinguish tumors with different ICI scores (AUC = 0.96, accuracy = 94%). This model was proven to be helpful for noninvasive prognostic stratification in an independent validation cohort. CONCLUSIONS ICI scores may serve as an effective prognostic biomarker to characterize potential biological processes in patients with GBM. This ICI signature can be evaluated noninvasively through radiogenomic analysis. KEY POINTS • Immune cell infiltration (ICI) scores can serve as an effective prognostic biomarker in patients with glioblastoma. • The ICI signature can be evaluated noninvasively through radiomic features derived from the intratumoral and peritumoral regions. • The prognostic value of the radiogenomic model can be verified by independent survival and MRI data.
Collapse
Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Honglin Ge
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Zhen Yan
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Bei Luo
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xinhua Hu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Kun Yang
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Yong Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Hongyi Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. .,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
| | - Wenbin Zhang
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. .,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
| |
Collapse
|
21
|
Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
Collapse
|
22
|
Vagvala S, Guenette JP, Jaimes C, Huang RY. Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics. Cancer Imaging 2022; 22:19. [PMID: 35436952 PMCID: PMC9014574 DOI: 10.1186/s40644-022-00455-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/29/2022] [Indexed: 01/12/2023] Open
Abstract
Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.
Collapse
Affiliation(s)
- Saivenkat Vagvala
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Jeffrey P Guenette
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Camilo Jaimes
- Division of Neuroradiology, Boston Children's, 300 Longwood Ave., 2nd floor, Main Building, Boston, MA, 02115, USA
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA.
| |
Collapse
|
23
|
Corr F, Grimm D, Saß B, Pojskić M, Bartsch JW, Carl B, Nimsky C, Bopp MHA. Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review. J Pers Med 2022; 12:jpm12030402. [PMID: 35330402 PMCID: PMC8952807 DOI: 10.3390/jpm12030402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/10/2022] Open
Abstract
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
Collapse
Affiliation(s)
- Felix Corr
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
- Correspondence:
| | - Dustin Grimm
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Mirza Pojskić
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Jörg W. Bartsch
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| |
Collapse
|
24
|
Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022; 66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its application to pan-cancer radiogenomics, which are extendibility and explainability.
Collapse
Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
| |
Collapse
|
25
|
Haubold J, Hosch R, Parmar V, Glas M, Guberina N, Catalano OA, Pierscianek D, Wrede K, Deuschl C, Forsting M, Nensa F, Flaschel N, Umutlu L. Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas. Cancers (Basel) 2021; 13:cancers13246186. [PMID: 34944806 PMCID: PMC8699054 DOI: 10.3390/cancers13246186] [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: 11/10/2021] [Revised: 11/23/2021] [Accepted: 11/28/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. METHODS MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. RESULTS The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. CONCLUSION This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding.
Collapse
Affiliation(s)
- Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
- Correspondence: ; Tel.: +49-201-723-84528; Fax: +49-201-723-1548
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Vicky Parmar
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Martin Glas
- Department of Neurology, Division of Clinical Neurooncology, University Hospital Essen, D-45147 Essen, Germany;
| | - Nika Guberina
- Department of Radiotherapy, University Hospital Essen, D-45147 Essen, Germany;
| | - Onofrio Antonio Catalano
- Department of Radiology, Division of Abdominal Imaging, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard University Medical School, Boston 02114, MA, USA;
| | - Daniela Pierscianek
- Department of Neurosurgery, University Hospital Essen, D-45147 Essen, Germany; (D.P.); (K.W.)
| | - Karsten Wrede
- Department of Neurosurgery, University Hospital Essen, D-45147 Essen, Germany; (D.P.); (K.W.)
| | - Cornelius Deuschl
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Nils Flaschel
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| |
Collapse
|
26
|
Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. PRECISION CLINICAL MEDICINE 2021; 4:271-286. [PMID: 35692858 PMCID: PMC8982528 DOI: 10.1093/pcmedi/pbab026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.
Collapse
Affiliation(s)
- Chong Zhang
- Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangyang Zhu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Tong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongyang Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
| |
Collapse
|
27
|
Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021; 13:cancers13235921. [PMID: 34885031 PMCID: PMC8656630 DOI: 10.3390/cancers13235921] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care. Abstract Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
Collapse
Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Stephen J. Bagley
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjay Saxena
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sina Bagheri
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Jun Guo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Ali Nabavizadeh
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - MacLean P. Nasrallah
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
| |
Collapse
|
28
|
Gore S, Chougule T, Jagtap J, Saini J, Ingalhalikar M. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization. Acad Radiol 2021; 28:1599-1621. [PMID: 32660755 DOI: 10.1016/j.acra.2020.06.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/11/2020] [Accepted: 06/11/2020] [Indexed: 12/22/2022]
Abstract
Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.
Collapse
|
29
|
Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
Collapse
Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
| |
Collapse
|
30
|
Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
Collapse
Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
| |
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
Collapse
Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
| |
Collapse
|
33
|
Kinoshita M, Kanemura Y, Narita Y, Kishima H. Reverse Engineering Glioma Radiomics to Conventional Neuroimaging. Neurol Med Chir (Tokyo) 2021; 61:505-514. [PMID: 34373429 PMCID: PMC8443974 DOI: 10.2176/nmc.ra.2021-0133] [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] [Indexed: 11/20/2022] Open
Abstract
A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.
Collapse
Affiliation(s)
- Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University.,Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neurosurgery, Osaka International Cancer Institute
| | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital
| | - Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine
| |
Collapse
|
34
|
Liu D, Chen J, Hu X, Yang K, Liu Y, Hu G, Ge H, Zhang W, Liu H. Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. Front Oncol 2021; 11:699265. [PMID: 34295824 PMCID: PMC8290166 DOI: 10.3389/fonc.2021.699265] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/23/2021] [Indexed: 12/12/2022] Open
Abstract
Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.
Collapse
Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenbin Zhang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| |
Collapse
|
35
|
Rathore S, Chaddad A, Iftikhar MA, Bilello M, Abdulkadir A. Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma. Radiol Imaging Cancer 2021; 3:e200108. [PMID: 34296969 DOI: 10.1148/rycan.2021200108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Purpose To test the hypothesis that combined features from MR and digital histopathologic images more accurately predict overall survival (OS) in patients with glioma compared with MRI or histopathologic features alone. Materials and Methods Multiparametric MR and histopathologic images in patients with a diagnosis of glioma (high- or low-grade glioma [HGG or LGG]) were obtained from The Cancer Imaging Archive (original images acquired 1983-2008). An extensive set of engineered features such as intensity, histogram, and texture were extracted from delineated tumor regions in MR and histopathologic images. Cox proportional hazard regression and support vector machine classification (SVC) models were applied to (a) MRI features only (MRIcox/svc), histopathologic features only (HistoPathcox/svc), and (c) combined MRI and histopathologic features (MRI+HistoPathcox/svc) and evaluated in a split train-test configuration. Results A total of 171 patients (mean age, 51 years ± 15; 91 men) were included with HGG (n = 75) and LGG (n = 96). Median OS was 467 days (range, 3-4752 days) for all patients, 350 days (range, 15-1561 days) for HGG, and 595 days (range, 3-4752 days) for LGG. The MRI+HistoPathcox model demonstrated higher concordance index (C-index) compared with MRIcox and HistoPathcox models on all patients (C-index, 0.79 vs 0.70 [P = .02; MRIcox] and 0.67 [P = .01; HistoPathcox]), patients with HGG (C-index, 0.78 vs 0.68 [P = .03; MRIcox] and 0.64 [P = .01; HistoPathcox]), and patients with LGG (C-index, 0.88 vs 0.62 [P = .008; MRIcox] and 0.62 [P = .006; HistoPathcox]). In binary classification, the MRI+HistoPathsvc model (area under the receiver operating characteristic curve [AUC], 0.86 [95% CI: 0.80, 0.95]) had higher performance than the MRIsvc model (AUC, 0.68 [95% CI: 0.50, 0.81]; P = .01) and the HistoPathsvc model (AUC, 0.72 [95% CI: 0.60, 0.85]; P = .04). Conclusion The model combining features from MR and histopathologic images had higher accuracy in predicting OS compared with the models with MR or histopathologic images alone. Keywords: Survival Prediction, Gliomas, Digital Pathology Imaging, MR Imaging, Machine Learning Supplemental material is available for this article.
Collapse
Affiliation(s)
- Saima Rathore
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Ahmad Chaddad
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Muhammad A Iftikhar
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Michel Bilello
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| | - Ahmed Abdulkadir
- From the Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Philadelphia, PA 19104 (S.R., M.B., A.A.); School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, China (A.C.); Comsats University Islamabad, Lahore Campus, Lahore, Pakistan (M.A.I.); and University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland (A.A.)
| |
Collapse
|
36
|
La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
Collapse
Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| |
Collapse
|
37
|
Buchlak QD, Esmaili N, Leveque JC, Bennett C, Farrokhi F, Piccardi M. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci 2021; 89:177-198. [PMID: 34119265 DOI: 10.1016/j.jocn.2021.04.043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/30/2021] [Indexed: 12/13/2022]
Abstract
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
Collapse
Affiliation(s)
- Quinlan D Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia.
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
| | | | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
| | - Massimo Piccardi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
| |
Collapse
|
38
|
Abstract
The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.
Collapse
Affiliation(s)
- Chaitra Badve
- Department of Radiology, Division of Neuroradiology, University Hospitals Cleveland Medical Center, BSH 5056, 11100 Euclid Avenue, Cleveland, OH 44106, USA.
| | - Sangam Kanekar
- Department of Radiology and Neurology, Division of Neuroradiology, Penn State College of Medicine, Penn State Milton Hershey Medical Center, Mail Code H066 500, University Drive, Hershey, PA 17033, USA
| |
Collapse
|
39
|
Jian A, Jang K, Manuguerra M, Liu S, Magnussen J, Di Ieva A. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. Neurosurgery 2021; 89:31-44. [PMID: 33826716 DOI: 10.1093/neuros/nyab103] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/24/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Molecular characterization of glioma has implications for prognosis, treatment planning, and prediction of treatment response. Current histopathology is limited by intratumoral heterogeneity and variability in detection methods. Advances in computational techniques have led to interest in mining quantitative imaging features to noninvasively detect genetic mutations. OBJECTIVE To evaluate the diagnostic accuracy of machine learning (ML) models in molecular subtyping gliomas on preoperative magnetic resonance imaging (MRI). METHODS A systematic search was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify studies up to April 1, 2020. Methodological quality of studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS)-2. Diagnostic performance estimates were obtained using a bivariate model and heterogeneity was explored using metaregression. RESULTS Forty-four original articles were included. The pooled sensitivity and specificity for predicting isocitrate dehydrogenase (IDH) mutation in training datasets were 0.88 (95% CI 0.83-0.91) and 0.86 (95% CI 0.79-0.91), respectively, and 0.83 to 0.85 in validation sets. Use of data augmentation and MRI sequence type were weakly associated with heterogeneity. Both O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation and 1p/19q codeletion could be predicted with a pooled sensitivity and specificity between 0.76 and 0.83 in training datasets. CONCLUSION ML application to preoperative MRI demonstrated promising results for predicting IDH mutation, MGMT methylation, and 1p/19q codeletion in glioma. Optimized ML models could lead to a noninvasive, objective tool that captures molecular information important for clinical decision making. Future studies should use multicenter data, external validation and investigate clinical feasibility of ML models.
Collapse
Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Kevin Jang
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Discipline of Surgery, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Maurizio Manuguerra
- Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Centre for Health Informatics, Macquarie University, Sydney, Australia
| | - John Magnussen
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Macquarie Medical Imaging, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Macquarie Neurosurgery, Macquarie University, Sydney, Australia
| |
Collapse
|
40
|
Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2021; 12658:157-167. [PMID: 34514469 DOI: 10.1007/978-3-030-72084-1_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Glioblastoma ( GBM ) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Here we present a scheme to accelerate the computation. In particular, we present a deep learning ( DL )-based logistic regression model to estimate the GBM's biophysical growth in seconds. This growth is defined by three tumor-specific parameters: 1) a diffusion coefficient in white matter ( Dw ), which prescribes the rate of infiltration of tumor cells in white matter, 2) a mass-effect parameter ( Mp ), which defines the average tumor expansion, and 3) the estimated time ( T ) in number of days that the tumor has been growing. Preoperative structural multi-parametric MRI ( mpMRI ) scans from n = 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for each of the tumor-specific growth parameters. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. The average Pearson correlation coefficients between our DL-based estimations and the biophysical parameters are 0.85 for Dw, 0.90 for Mp, and 0.94 for T, respectively. This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation. It paves the way towards their clinical translation and opens the door for leveraging advanced radiomic descriptors in future studies by means of a significantly faster parameter reconstruction compared to biophysical growth modeling approaches.
Collapse
|
41
|
d’Este SH, Nielsen MB, Hansen AE. Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature. Diagnostics (Basel) 2021; 11:diagnostics11040592. [PMID: 33806195 PMCID: PMC8067218 DOI: 10.3390/diagnostics11040592] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 01/14/2023] Open
Abstract
The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.
Collapse
Affiliation(s)
- Sabrina Honoré d’Este
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Correspondence:
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| |
Collapse
|
42
|
Segura-Collar B, Garranzo-Asensio M, Herranz B, Hernández-SanMiguel E, Cejalvo T, Casas BS, Matheu A, Pérez-Núñez Á, Sepúlveda-Sánchez JM, Hernández-Laín A, Palma V, Gargini R, Sánchez-Gómez P. Tumor-Derived Pericytes Driven by EGFR Mutations Govern the Vascular and Immune Microenvironment of Gliomas. Cancer Res 2021; 81:2142-2156. [PMID: 33593822 DOI: 10.1158/0008-5472.can-20-3558] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/28/2020] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
The extraordinary plasticity of glioma cells allows them to contribute to different cellular compartments in tumor vessels, reinforcing the vascular architecture. It was recently revealed that targeting glioma-derived pericytes, which represent a big percentage of the mural cell population in aggressive tumors, increases the permeability of the vessels and improves the efficiency of chemotherapy. However, the molecular determinants of this transdifferentiation process have not been elucidated. Here we show that mutations in EGFR stimulate the capacity of glioma cells to function as pericytes in a BMX- (bone marrow and X-linked) and SOX9-dependent manner. Subsequent activation of platelet-derived growth factor receptor beta in the vessel walls of EGFR-mutant gliomas stabilized the vasculature and facilitated the recruitment of immune cells. These changes in the tumor microenvironment conferred a growth advantage to the tumors but also rendered them sensitive to pericyte-targeting molecules such as ibrutinib or sunitinib. In the absence of EGFR mutations, high-grade gliomas were enriched in blood vessels, but showed a highly disrupted blood-brain barrier due to the decreased BMX/SOX9 activation and pericyte coverage, which led to poor oxygenation, necrosis, and hypoxia. Overall, these findings identify EGFR mutations as key regulators of the glioma-to-pericyte transdifferentiation, highlighting the intricate relationship between the tumor cells and their vascular and immune milieu. Our results lay the foundations for a vascular-dependent stratification of gliomas and suggest different therapeutic vulnerabilities determined by the genetic status of EGFR. SIGNIFICANCE: This study identifies the EGFR-related mechanisms that govern the capacity of glioma cells to transdifferentiate into pericytes, regulating the vascular and immune phenotypes of the tumors. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/8/2142/F1.large.jpg.
Collapse
Affiliation(s)
- Berta Segura-Collar
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - María Garranzo-Asensio
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Beatriz Herranz
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Facultad de Medicina, Universidad Francisco de Vitoria, Madrid, Spain
| | - Esther Hernández-SanMiguel
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Teresa Cejalvo
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Bárbara S Casas
- Laboratory of Stem Cells and Developmental Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Ander Matheu
- Cellular Oncology Group, Biodonostia Health Research Institute, San Sebastian, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
| | - Ángel Pérez-Núñez
- Dto. Neurocirugía, Hospital 12 de Octubre, Universidad Complutense de Madrid, Madrid, Spain
| | | | | | - Verónica Palma
- Laboratory of Stem Cells and Developmental Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Ricardo Gargini
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Pilar Sánchez-Gómez
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| |
Collapse
|
43
|
Venet L, Pati S, Feldman MD, Nasrallah MP, Yushkevich P, Bakas S. Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration. APPLIED SCIENCES (BASEL, SWITZERLAND) 2021; 11:1892. [PMID: 34290888 PMCID: PMC8291745 DOI: 10.3390/app11041892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.
Collapse
Affiliation(s)
- Ludovic Venet
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael D. Feldman
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - MacLean P. Nasrallah
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul Yushkevich
- Center for Biomedical Image Computing & Analytics, 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 & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
44
|
Park CJ, Han K, Kim H, Ahn SS, Choi D, Park YW, Chang JH, Kim SH, Cha S, Lee SK. MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas. AJNR Am J Neuroradiol 2021; 42:448-456. [PMID: 33509914 DOI: 10.3174/ajnr.a6983] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE Isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas (histologic grades II and III) with epidermal growth factor receptor (EGFR) amplification or telomerase reverse transcriptase (TERT) promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma. MATERIALS AND METHODS In this multi-institutional retrospective study, pathologically confirmed IDH wild-type lower-grade gliomas from 2 tertiary institutions and The Cancer Genome Atlas constituted the training set (institution 1 and The Cancer Genome Atlas, 64 patients) and the independent test set (institution 2, 57 patients). Preoperative MRIs were analyzed using the Visually AcceSAble Rembrandt Images and radiomics. The molecular glioblastoma status was determined on the basis of the presence of EGFR amplification and TERT promoter mutation. Molecular glioblastoma was present in 73.4% and 56.1% in the training and test sets, respectively. Models using clinical, Visually AcceSAble Rembrandt Images, and radiomic features were built to predict the molecular glioblastoma status in the training set; then they were validated in the test set. RESULTS In the test set, a model using both Visually AcceSAble Rembrandt Images and radiomic features showed superior predictive performance (area under the curve = 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve = 0.514 and 0.648, respectively; P < . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve = 0.514 versus 0.863, P < .001). CONCLUSIONS MR imaging features integrated with machine learning classifiers may predict a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.
Collapse
Affiliation(s)
- C J Park
- From the Department of Radiology (C.J.P.), Yonsei University College of Medicine, Seoul, Korea
| | - K Han
- Department of Radiology (K.H., H.K., S.S.A., Y.W.P., S.-K.L.), Research Institute of Radiological Sciences, Center for Clinical Imaging Data Science
| | - H Kim
- Department of Radiology (K.H., H.K., S.S.A., Y.W.P., S.-K.L.), Research Institute of Radiological Sciences, Center for Clinical Imaging Data Science
| | - S S Ahn
- Department of Radiology (K.H., H.K., S.S.A., Y.W.P., S.-K.L.), Research Institute of Radiological Sciences, Center for Clinical Imaging Data Science
| | - D Choi
- Department of Computer Science (D.C.), Yonsei University, Seoul, Korea
| | - Y W Park
- Department of Radiology (K.H., H.K., S.S.A., Y.W.P., S.-K.L.), Research Institute of Radiological Sciences, Center for Clinical Imaging Data Science
| | | | - S H Kim
- Department of Pathology (S.H.K.), Yonsei University College of Medicine, Seoul, Korea
| | - S Cha
- Department of Radiology and Biomedical Imaging (S.C.), University of California San Francisco, San Francisco, California
| | - S-K Lee
- Department of Radiology (K.H., H.K., S.S.A., Y.W.P., S.-K.L.), Research Institute of Radiological Sciences, Center for Clinical Imaging Data Science
| |
Collapse
|
45
|
Sanvito F, Castellano A, Falini A. Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers (Basel) 2021; 13:cancers13030424. [PMID: 33498680 PMCID: PMC7865835 DOI: 10.3390/cancers13030424] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/15/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored. Abstract In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
Collapse
Affiliation(s)
- Francesco Sanvito
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Correspondence: ; Tel.: +39-02-2643-3015
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
| |
Collapse
|
46
|
Rathore S, Mohan S, Bakas S, Sako C, Badve C, Pati S, Singh A, Bounias D, Ngo P, Akbari H, Gastounioti A, Bergman M, Bilello M, Shinohara RT, Yushkevich P, O'Rourke DM, Sloan AE, Kontos D, Nasrallah MP, Barnholtz-Sloan JS, Davatzikos C. Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk). Neurooncol Adv 2021; 2:iv22-iv34. [PMID: 33521638 PMCID: PMC7829474 DOI: 10.1093/noajnl/vdaa128] [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] [Indexed: 12/13/2022] Open
Abstract
Background Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm. Methods We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70-30% training-testing splits or 10-fold cross-validation within individual collections, (2) 70-30% training-testing splits within merged collections, and (3) training on one collection and testing on another. Results These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset. Conclusions Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.
Collapse
Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, Ohio, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dimitrios Bounias
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Phuc Ngo
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul Yushkevich
- Penn Image Computing and Science Lab (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Donald M O'Rourke
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, Pennsylvania, USA
| | - Andrew E Sloan
- Case Comprehensive Cancer Center, Cleveland, Ohio, USA.,Department of Neurological Surgery, University Hospitals Seidman Cancer Center, Cleveland, Ohio, USA.,Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center, Cleveland, Ohio, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
47
|
Riva M, Lopci E, Gay LG, Nibali MC, Rossi M, Sciortino T, Castellano A, Bello L. Advancing Imaging to Enhance Surgery: From Image to Information Guidance. Neurosurg Clin N Am 2021; 32:31-46. [PMID: 33223024 DOI: 10.1016/j.nec.2020.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.
Collapse
Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, Milan 20122, Italy; IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy.
| | - Egesta Lopci
- Unit of Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan 20089, Italy. https://twitter.com/LopciEgesta
| | - Lorenzo G Gay
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Marco Conti Nibali
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy. https://twitter.com/dr_mcn
| | - Marco Rossi
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Tommaso Sciortino
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20123, Italy. https://twitter.com/antocastella
| | - Lorenzo Bello
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| |
Collapse
|
48
|
Pati S, Verma R, Akbari H, Bilello M, Hill VB, Sako C, Correa R, Beig N, Venet L, Thakur S, Serai P, Ha SM, Blake GD, Shinohara RT, Tiwari P, Bakas S. Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset. Med Phys 2020; 47:6039-6052. [PMID: 33118182 DOI: 10.1002/mp.14556] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/26/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites. ACQUISITION AND VALIDATION METHODS From TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. DATA FORMAT AND USAGE NOTES We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis. POTENTIAL APPLICATIONS The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.
Collapse
Affiliation(s)
- 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
| | - Ruchika Verma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Hamed Akbari
- 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
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Virginia B Hill
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Chiharu Sako
- 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
| | - Ramon Correa
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Ludovic Venet
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Siddhesh Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Prashant Serai
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Computer Science and Engineering, The Ohio State University, OH, 43210, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Geri D Blake
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Russell Taki Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn Statistical Imaging and Visualization Endeavor (PennSIVE), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, 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
| |
Collapse
|
49
|
Fathi Kazerooni A, Akbari H, Shukla G, Badve C, Rudie JD, Sako C, Rathore S, Bakas S, Pati S, Singh A, Bergman M, Ha SM, Kontos D, Nasrallah M, Bagley SJ, Lustig RA, O'Rourke DM, Sloan AE, Barnholtz-Sloan JS, Mohan S, Bilello M, Davatzikos C. Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma. JCO Clin Cancer Inform 2020; 4:234-244. [PMID: 32191542 PMCID: PMC7113126 DOI: 10.1200/cci.19.00121] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
PURPOSE To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis. PATIENTS AND METHODS We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort. RESULTS These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP. CONCLUSION Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.
Collapse
Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center and Research Institute, Newark, DE.,Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Chaitra Badve
- Department of Radiology, University Hospitals-Seidman Cancer Center, Cleveland, OH.,Case Comprehensive Cancer Center, Cleveland, OH
| | - Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - MacLean Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephen J Bagley
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Donald M O'Rourke
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
| | - Andrew E Sloan
- Case Western Reserve University School of Medicine, Cleveland, OH.,Case Comprehensive Cancer Center, Cleveland, OH.,Department of Neurologic Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH
| | - Jill S Barnholtz-Sloan
- Case Western Reserve University School of Medicine, Cleveland, OH.,Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
50
|
Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020; 47:4265-4280. [PMID: 32615647 DOI: 10.1002/mp.14368] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction. METHODS We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%). RESULTS In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test-retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5-4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. CONCLUSION Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models.
Collapse
Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Science, Kerman, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada.,Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada
| |
Collapse
|