Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma
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PURPOSE: To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis. PATIENTS AND METHODS: We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort. RESULTS: These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP. CONCLUSION: Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.
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Varying characteristics of the multi-parametric MRI data due to scanner hardware and acquisition protocol differences were handled at each collaborating site via established harmonized preprocessing pipelines.
“Moreover, varying characteristics of the mpMRI data due to scanner hardware and acquisition protocol differences33,34 were handled at each collaborating site via established harmonized preprocessing pipelines35–39.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [34]
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- Authors
- Fathi Kazerooni, A., Akbari, H., Shukla, G., Badve, C., Rudie, J. D., Sako, C., Rathore, S., Bakas, S., Pati, S., Singh, A., Bergman, M., Ha, S. M., Kontos, D., Nasrallah, M., Bagley, S. J., Lustig, R. A., O’Rourke, D. M., Sloan, A. E., Barnholtz-Sloan, J. S., Mohan, S., Bilello, M., & Davatzikos, C.
- Affiliations
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- University of Pennsylvania (US)
- University Hospitals Seidman Cancer Center (US)
- University of California, San Francisco (US)
- Abramson Cancer Center (US)
- Case Western Reserve University School of Medicine, Cleveland, OH
- Corresponding author
- Davatzikos, Christos
- ORCID
- Venue
- JCO Clinical Cancer Informatics
- Published
- 2020-11
- Volume
- 4
- Issue
- 4
- Pages
- 234-244
- ISSN
- 2473-4276 (ISSN-L)
- PMC
- PMC7113126
- OpenAlex
- W3012393256
- Cited by
- 39
- References
- 36
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 10 · 2021: 9 · 2020: 5
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- JCO Clinical Cancer Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1s10od023495-01)
- National Cancer Institute (U24CA189523)
- National Institute of Neurological Disorders and Stroke (R01NS042645)
- National Institute of Biomedical Imaging and Bioengineering (T32EB004311)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- glioblastoma, radiomics, progression-free survival prediction, multiparametric MRI, preoperative imaging, chemoradiotherapy, radiogenomics
- MeSH terms
- Progression-Free Survival, Phenomics, Multiparametric Magnetic Resonance Imaging, Adult, Aged, Aged, 80 and over, Algorithms, Brain Neoplasms, Female, Glioblastoma, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Neoplasm Recurrence, Local, Retrospective Studies, ROC Curve, Software, Survival Rate, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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