ArticleScientific reports2025
Predicting lateral pelvic lymph node metastasis in rectal cancer patients using MRI radiomics: a multicenter retrospective study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence Gap in Managing Lateral Pelvic Lymph Nodes in Rectal Cancer: a Systematic Review of Radiation Boost Strategies.Journal of gastrointestinal cancer · 2026Pooled it
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- MRI-Based Morphological Features as Predictors of Clinical Outcomes in Locally Advanced Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy: Insights from a Single-Institution Experience.Journal of clinical medicine · 2026Article
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8 authors.
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Abstract
MRI has relatively low sensitivity and specificity in detecting lymph node metastases. This study aimed to develop and validate an MRI radiomics-based model for predicting lateral pelvic lymph node (LPLN) metastasis in rectal cancer patients who underwent LPLN dissection, and to compare its performance with that of radiologists. This multicenter retrospective study included 336 rectal cancer patients (199 men; mean age, 58.9 years ± 11.1 [standard deviation]) who underwent LPLN dissection. Patients were divided into development (n = 190) and validation (n = 146) cohorts. Radiomics features were extracted from MR images, and the Least Absolute Shrinkage and Selection Operator regression was used to construct radiomics and clinical-radiomics models. Model performance was compared with radiologists using receiver operating characteristic (ROC) analysis. Malignant LPLN was diagnosed in 32.4% of the development cohort (65/190) and 32.9% of the validation cohort (48/146) (P = 0.798). Seven radiomics features and two clinical features were selected. The radiomics and clinical-radiomics models demonstrated area under the curves (AUCs) of 0.819 and 0.830 in the development cohort and 0.821 and 0.829 in the validation cohort, respectively. The optimal cut-off (- 0.47) yielded sensitivities of 72.3% and 45.8% and specificities of 82.4% and 87.8% in the development and validation cohorts, respectively. Decision curve analysis indicated no additional net benefit from the clinical-radiomics model compared to the radiomics-only model. Radiologists' AUCs were significantly lower than that of the radiomics model (0.842) and improved with radiomics probability scores (0.734 vs. 0.801; 0.668 vs. 0.791). The MRI-based radiomics model significantly improves the prediction of LPLN metastasis in rectal cancer, outperforming conventional criteria used by radiologists.Trial registration: Retrospectively registered.
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