Cited by 204Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisAI in cancer detection

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

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Davatzikos, Christos, Rathore, Saima, Bakas, Spyridon, Pati, Sarthak, Bergman, Mark, Kalarot, Ratheesh, Sridharan, Patmaa, Gastounioti, Aimilia, Jahani, Nariman, Cohen, Eric, Akbari, Hamed, Tunc, Birkan, Doshi, Jimit, Parker, Drew, Hsieh, Michael, Sotiras, Aristeidis, Li, Hongming, Ou, Yangming, Doot, Robert K., Bilello, Michel, Fan, Yong, Shinohara, Russell T., Yushkevich, Paul, Verma, Ragini, Kontos, Despina·2018·Journal of Medical Imaging

DOI 10.1117/1.jmi.5.1.011018PMC PMC5764116

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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.

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The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
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Authors: 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, R. K., Bilello, M., Fan, Y., Shinohara, R. T., Yushkevich, P., Verma, R., & Kontos, D.
Affiliations: University of Pennsylvania (US)
Univ. of Pennsylvania
Massachusetts General Hospital (US)
ORCID: Davatzikos, Christos
Rathore, Saima
Bakas, Spyridon
Pati, Sarthak
Bergman, Mark
Gastounioti, Aimilia
Cohen, Eric
Akbari, Hamed
Doshi, Jimit
Parker, Drew
Sotiras, Aristeidis
Li, Hongming
Ou, Yangming
Doot, Robert K.
Bilello, Michel
Shinohara, Russell T.
Yushkevich, Paul
Verma, Ragini
Venue: Journal of Medical Imaging
Publisher: SPIE-Intl Soc Optical Eng
Published: 2018-01-11
Volume: 5
Issue: 01
Pages: 1-21
ISSN: 2329-4302 (ISSN-L)
2329-4310
DOI: 10.1117/1.jmi.5.1.011018
PMC: PMC5764116
OpenAlex: W2783923767
Cited by: 204
References: 76
Citations by year: 2026: 9 · 2025: 30 · 2024: 32 · 2023: 34 · 2022: 26 · 2021: 26 · 2020: 23 · 2019: 18 · 2018: 6
FWCI: 12.49
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Journal of medical imaging (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Cancer Institute (U24CA189523-01A1)
US National Institutes of Health (R01NS042645-11A1, R01NS096606, R01CA197000-02)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: radiographic image analysis, brain glioma, breast cancer, lung cancer, treatment response prediction, prognostic factors, predictive biomarkers, texture analysis, connectomics, machine learning, precision diagnostics, risk assessment

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

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