Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

被引：158

作者：

Davatzikos, Christos ^{[1
]}

Rathore, Saima ^{[1
]}

Bakas, Spyridon ^{[1
]}

Pati, Sarthak ^{[1
]}

Bergman, Mark ^{[1
]}

Kalarot, Ratheesh ^{[1
]}

Sridharan, Patmaa ^{[1
]}

Gastounioti, Aimilia ^{[1
]}

Jahani, Nariman ^{[1
]}

Cohen, Eric ^{[1
]}

Akbari, Hamed ^{[1
]}

Tunc, Birkan ^{[1
]}

Doshi, Jimit ^{[1
]}

Parker, Drew ^{[1
]}

Hsieh, Michael ^{[1
]}

Sotiras, Aristeidis ^{[1
]}

Li, Hongming ^{[1
]}

Ou, Yangming ^{[2
]}

Doot, Robert K. ^{[1
]}

Bilello, Michel ^{[1
]}

Fan, Yong ^{[1
]}

Shinohara, Russell T. ^{[1
,3
]}

Yushkevich, Paul ^{[1
]}

Verma, Ragini ^{[1
]}

Kontos, Despina ^{[1
]}

机构：

[1] CBICA, Philadelphia, PA 19104 USA

[2] Massachusetts Gen Hosp, Martinos Ctr Biomed Imaging, Boston, MA 02114 USA

[3] Univ Penn, Perelman Sch Med, CCEB, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA

来源：

JOURNAL OF MEDICAL IMAGING | 2018年 / 5卷 / 01期

关键词：

cancer imaging phenomics; radiomics; radiogenomics; precision diagnostics; treatment response; open source software;

D O I：

10.1117/1.JMI.5.1.011018

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

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. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

引用

页数：21

共 73 条

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