TY - JOUR
T1 - Cancer imaging phenomics toolkit
T2 - Quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
AU - Davatzikos, Christos
AU - Rathore, Saima
AU - Bakas, Spyridon
AU - Pati, Sarthak
AU - Bergman, Mark
AU - Kalarot, Ratheesh
AU - Sridharan, Patmaa
AU - Gastounioti, Aimilia
AU - Jahani, Nariman
AU - Cohen, Eric
AU - Akbari, Hamed
AU - Tunc, Birkan
AU - Doshi, Jimit
AU - Parker, Drew
AU - Hsieh, Michael
AU - Sotiras, Aristeidis
AU - Li, Hongming
AU - Ou, Yangming
AU - Doot, Robert K.
AU - Bilello, Michel
AU - Fan, Yong
AU - Shinohara, Russell T.
AU - Yushkevich, Paul
AU - Verma, Ragini
AU - Kontos, Despina
N1 - Publisher Copyright:
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - cancer imaging phenomics
KW - open source software
KW - precision diagnostics
KW - radiogenomics
KW - radiomics
KW - treatment response
UR - http://www.scopus.com/inward/record.url?scp=85040819784&partnerID=8YFLogxK
U2 - 10.1117/1.JMI.5.1.011018
DO - 10.1117/1.JMI.5.1.011018
M3 - Article
C2 - 29340286
AN - SCOPUS:85040819784
SN - 2329-4302
VL - 5
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 1
M1 - 011018
ER -