ArticleAnnals of surgical oncology2025
Preoperative Assessment of Lymph Node Metastasis in Rectal Cancer Using Deep Learning: Investigating the Utility of Various MRI Sequences.
Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multiparametric MRI-based multi-channel deep learning model for accurate preoperative prediction of perineural invasion in lymph node-negative rectal cancer.Abdominal radiology (New York) · 2026Article
- Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.BMC cancer · 2026Article
- Preoperative prediction of lymphatic metastasis in rectal cancer using a fusion model based on multiparameter magnetic resonance imaging: a retrospective validation study.Frontiers in oncology · 2026Article
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9 authors.
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Abstract
purposeThis study aimed to develop a deep learning (DL) model based on three-dimensional multi-parametric magnetic resonance imaging (mpMRI) for preoperative assessment of lymph node metastasis (LNM) in rectal cancer (RC) and to investigate the contribution of different MRI sequences. PATIENTS AND
methodsA total of 613 eligible patients with RC from four medical centres who underwent preoperative mpMRI were retrospectively enrolled and randomly assigned to training (n = 372), validation (n = 106), internal test (n = 88) and external test (n = 47) cohorts. A multi-parametric multi-scale EfficientNet (MMENet) was designed to effectively extract LNM-related features from mpMR for preoperative LNM assessment. Its performance was compared with other DL models and radiologists using metrics of area under the receiver operating curve (AUC), accuracy (ACC), sensitivity, specificity and average precision with 95% confidence interval (CI). To investigate the utility of various MRI sequences, the performances of the mono-parametric model and the MMENet with different sequences combinations as input were compared.
resultsThe MMENet using a combination of T2WI, DWI and DCE sequence achieved an AUC of 0.808 (95% CI 0.720-0.897) with an ACC of 71.6% (95% CI 62.3-81.0) in the internal test cohort and an AUC of 0.782 (95% CI 0.636-0.925) with an ACC of 76.6% (95% CI 64.6-88.6) in the external test cohort, outperforming the mono-parametric model, the MMENet with other sequences combinations and the radiologists.
conclusionsThe MMENet, leveraging a combination of T2WI, DWI and DCE sequences, can accurately assess LNM in RC preoperatively and holds great promise for automated evaluation of LNM in clinical practice.
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