The DOI matches its Crossref record; 75 of the 76 references it cites were found in a registry or an index.
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
Open accessAbstract
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 BraTS challenge35–38, seeking methodological advancements in the domain of neuro-oncology, has been providing the community with (i) the largest publicly available and manually-curated mpMRI dataset of diffuse glioma patients (an example of which is illustrated in Fig. 1b), and (ii) a harmonized preprocessing pipeline51,70,71 to handle differences in inter-site acquisition protocols.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [51]
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Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Cortazar, P., Zhang, L., Untch, M., Mehta, K. M., Costantino, J. P., Wolmark, N., Bonnefoi, H. R., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,777
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Self-citationPaik, S., Tang, G., Shak, S., Kim, C., Baker, J. B., Kim, W., Cronin, M. T., Baehner, F. L., Watson, D., Bryant, J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.7985 · Cited by 2,597
Texture analysis using gray level run lengths
Galloway, M. M. (1975) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(75)80008-6 · Cited by 2,168
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Implementing AI in Neuroradiology
Shanker, R. R. B. J., Shetty, A., & Ginat, D. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-24568-7_2
Metadata
- 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
- 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
- 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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