11 January 2018 Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
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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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Christos Davatzikos, Christos Davatzikos, Saima Rathore, Saima Rathore, Spyridon Bakas, Spyridon Bakas, Sarthak Pati, Sarthak Pati, Mark Bergman, Mark Bergman, Ratheesh Kalarot, Ratheesh Kalarot, Patmaa Sridharan, Patmaa Sridharan, Aimilia Gastounioti, Aimilia Gastounioti, Nariman Jahani, Nariman Jahani, Eric Cohen, Eric Cohen, Hamed Akbari, Hamed Akbari, Birkan Tunc, Birkan Tunc, Jimit Doshi, Jimit Doshi, Drew Parker, Drew Parker, Michael Hsieh, Michael Hsieh, Aristeidis Sotiras, Aristeidis Sotiras, Hongming Li, Hongming Li, Yangming Ou, Yangming Ou, Robert K. Doot, Robert K. Doot, Michel Bilello, Michel Bilello, Yong Fan, Yong Fan, Russell T. Shinohara, Russell T. Shinohara, Paul Yushkevich, Paul Yushkevich, Ragini Verma, Ragini Verma, Despina Kontos, Despina Kontos, } "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome," Journal of Medical Imaging 5(1), 011018 (11 January 2018). https://doi.org/10.1117/1.JMI.5.1.011018 . Submission: Received: 30 June 2017; Accepted: 5 December 2017
Received: 30 June 2017; Accepted: 5 December 2017; Published: 11 January 2018
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