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Journal of Medical Imaging • Open Access

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
Author(s): Christos Davatzikos; Saima Rathore; Spyridon Bakas; Sarthak Pati; Mark Bergman; Ratheesh Kalarot; Patmaa Sridharan; Aimilia Gastounioti; Nariman Jahani; Eric Cohen; Hamed Akbari; Birkan Tunc; Jimit Doshi; Drew Parker; Michael Hsieh; Aristeidis Sotiras; Hongming Li; Yangming Ou; Robert K. Doot; Michel Bilello; Yong Fan; Russell T. Shinohara; Paul Yushkevich; Ragini Verma; Despina Kontos

Paper 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.

Paper Details

Date Published: 11 January 2018
PDF: 21 pages
J. Med. Imag. 5(1) 011018 doi: 10.1117/1.JMI.5.1.011018
Published in: Journal of Medical Imaging Volume 5, Issue 1
Show Author Affiliations
Christos Davatzikos, Univ. of Pennsylvania (United States)
Saima Rathore, Univ. of Pennsylvania (United States)
Spyridon Bakas, Univ. of Pennsylvania (United States)
Sarthak Pati, Univ. of Pennsylvania (United States)
Mark Bergman, Univ. of Pennsylvania (United States)
Ratheesh Kalarot, Univ. of Pennsylvania (United States)
Patmaa Sridharan, Univ. of Pennsylvania (United States)
Aimilia Gastounioti, Univ. of Pennsylvania (United States)
Nariman Jahani, Univ. of Pennsylvania (United States)
Eric Cohen, Univ. of Pennsylvania (United States)
Hamed Akbari, Univ. of Pennsylvania (United States)
Birkan Tunc, Univ. of Pennsylvania (United States)
Jimit Doshi, Univ. of Pennsylvania (United States)
Drew Parker, Univ. of Pennsylvania (United States)
Michael Hsieh, Univ. of Pennsylvania (United States)
Aristeidis Sotiras, Univ. of Pennsylvania (United States)
Hongming Li, Univ. of Pennsylvania (United States)
Yangming Ou, Massachusetts General Hospital (United States)
Athinoula A. Martinos Ctr. for Biomedical Imaging (United States)
Robert K. Doot, Univ. of Pennsylvania (United States)
Michel Bilello, Univ. of Pennsylvania (United States)
Yong Fan, Univ. of Pennsylvania (United States)
Russell T. Shinohara, Univ. of Pennsylvania (United States)
Paul Yushkevich, Univ. of Pennsylvania (United States)
Ragini Verma, Univ. of Pennsylvania (United States)
Despina Kontos, Univ. of Pennsylvania (United States)


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