ArticleBMC medical informatics and decision making2025
Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prognostic value of quantitative multiparametric magnetic resonance imaging parameters and histopathologic features for disease-free survival in locally advanced non-mucinous rectal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Deep learning model based on MRI-derived microvascular network simulation parameters for noninvasive assessment of lymphovascular invasion in rectal cancer patients.Journal of gastrointestinal oncology · 2026Article
- Multitask learning for early treatment response and survival prediction in lung cancer radiotherapy using sequential CBCT imaging.Frontiers in oncology · 2026Article
- Editorial: Advances in medical imaging for precision diagnostic and therapeutic applications in digestive diseases.Frontiers in medicine · 2026Article
- Digital intelligence-enabled tumor microbiome research: from mechanistic dissection to clinical translation.Frontiers in oncology · 2026Review
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Authors and funding
16 authors.
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Abstract
backgroundPrognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.
methodsThis retrospective study enrolled 321 rectal cancer patients (training set: 212; internal testing set: 53; external testing set: 56) who directly received total mesorectal excision from five hospitals between March 2014 to April 2021. A multitask deep learning model was developed to simultaneously predict recurrence/metastasis and disease-free survival (DFS). The model integrated clinicopathologic data and multiparametric magnetic resonance imaging (MRI) images including diffusion kurtosis imaging (DKI), without performing tumor segmentation. The receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index) were used to evaluate the predictive performance of the proposed model.
resultsThe deep learning model achieved good discrimination capability of recurrence/metastasis, with area under the curve (AUC) values of 0.885, 0.846, and 0.797 in the training, internal testing and external testing sets, respectively. Furthermore, the model successfully predicted DFS in the training set (C-index: 0.812), internal testing set (C-index: 0.794), and external testing set (C-index: 0.733), and classified patients into significantly distinct high- and low-risk groups (p < 0.05).
conclusionsThe multitask deep learning model, incorporating clinicopathologic data and multiparametric MRI, effectively predicted both recurrence/metastasis and survival for patients with rectal cancer. It has the potential to be an essential tool for risk stratification, and assist in making individualized treatment decisions. CLINICAL TRIAL NUMBER: Not applicable.
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