An MRI deep learning model predicts outcome in rectal cancer
Open accessAbstract
Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model–computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46–64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47–63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52–70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50–67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.
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Prognostic AI models for colorectal cancer have been developed for MRI, histopathology, and multiplex imaging.
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- Authors
- Jiang, X., Zhao, H., Saldanha, O. L., Nebelung, S., Kuhl, C., Amygdalos, I., Lang, S. A., Wu, X., Meng, X., Truhn, D., Kather, J. N., & Ke, J.
- Affiliations
- Sun Yat-sen University (CN)
- ORCID
- Venue
- Radiology
- Published
- 2023-06-01
- Volume
- 307
- Issue
- 5
- Pages
- e222223-e222223
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W4379508286
- Cited by
- 127
- References
- 30
- Citations by year
- 2026: 29 · 2025: 64 · 2024: 30 · 2023: 4
- FWCI
- 21.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Radiology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RWTH Publications (RWTH Aachen) (publishedVersion, other-oa)
- Qucosa (Saxon State and University Library Dresden) (submittedVersion, cc-by)
- RWTH Publications (RWTH Aachen)
- Funders
- China Scholarship Council (202106380048)
- Bundesministerium für Gesundheit (DEEP LIVER, ZMVI1-2520DAT111)
- Deutsche Krebshilfe (70113864)
- Bundesministerium für Bildung und Forschung (PEARL 01KD2104C, CAMINO 01EO2101, SWAG 01KD2215A, TRANSFORM LIVER 031L0312A, TANGERINE 01KT2302 through ERA-NET Transcan)
- Deutscher Akademischer Austauschdienst (SECAI, 57616814)
- National Institute for Health and Care Research (NIHR213331)
- Guangdong Gastrointestinal Disease Research Center (2020B1111170004)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- rectal cancer, deep learning, outcome prediction, overall survival, preoperative MRI, T2-weighted MRI, tumor segmentation, tumor volume, carcinoembryonic antigen, risk stratification, multimodal models
- MeSH terms
- Deep Learning, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Rectal Neoplasms, Retrospective Studies, Risk Factors
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