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Davatzikos C, Sotiras A, Fan Y, Habes M, Erus G, Rathore S, Bakas S, Chitalia R, Gastounioti A, Kontos D. Precision diagnostics based on machine learning-derived imaging signatures. Magn Reson Imaging 2019; 64:49-61. [PMID: 31071473 PMCID: PMC6832825 DOI: 10.1016/j.mri.2019.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/24/2019] [Accepted: 04/29/2019] [Indexed: 01/08/2023]
Abstract
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
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Affiliation(s)
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America.
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Rhea Chitalia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
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Davatzikos C, Rathore S, Bakas S, Pati S, Bergman M, Kalarot R, Sridharan P, Gastounioti A, Jahani N, Cohen E, Akbari H, Tunc B, Doshi J, Parker D, Hsieh M, Sotiras A, Li H, Ou Y, Doot RK, Bilello M, Fan Y, Shinohara RT, Yushkevich P, Verma R, Kontos D. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 2018; 5:011018. [PMID: 29340286 PMCID: PMC5764116 DOI: 10.1117/1.jmi.5.1.011018] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 12/05/2017] [Indexed: 12/26/2022] Open
Abstract
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
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Affiliation(s)
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ratheesh Kalarot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Patmaa Sridharan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Nariman Jahani
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Eric Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Birkan Tunc
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Drew Parker
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michael Hsieh
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yangming Ou
- Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States
| | - Robert K. Doot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics (CCEB), Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania, United States
| | - Paul Yushkevich
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
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