ArticleInsights into imaging2025
An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer.
Article in Insights into imaging, 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.
- Review
- Attention-Enhanced ResNet-U-Net for Automated Colorectal Tumor Segmentation in CT Scans.Life (Basel, Switzerland) · 2026Article
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop an interpretable 3-year disease-free survival risk prediction tool to stratify patients with stage II colorectal cancer (CRC) by integrating CT images and clinicopathological factors.
methodsA total of 769 patients with pathologically confirmed stage II CRC and disease-free survival (DFS) follow-up information were recruited from three medical centers and divided into training (n = 442), test (n = 190), and validation cohorts (n = 137). CT-based tumor radiomics features were extracted, selected, and used to calculate a Radscore. A combined model was developed using artificial neural network (ANN) algorithm, by integrating the Radscore with significant clinicoradiological factors to classify patients into high- and low-risk groups. Model performance was assessed using the area under the curve (AUC), and feature contributions were qualified using the Shapley additive explanation (SHAP) algorithm. Kaplan-Meier survival analysis revealed the prognostic stratification value of the risk groups.
resultsFourteen radiomics features and five clinicoradiological factors were selected to construct the radiomics and clinicoradiological models, respectively. The combined model demonstrated optimal performance, with AUCs of 0.811 and 0.846 in the test and validation cohorts, respectively. Kaplan-Meier curves confirmed effective patient stratification (p < 0.001) in both test and validation cohorts. A high Radscore, rough intestinal outer edge, and advanced age were identified as key prognostic risk factors using the SHAP.
conclusionThe combined model effectively stratified patients with stage II CRC into different prognostic risk groups, aiding clinical decision-making. CRITICAL RELEVANCE STATEMENT: Integrating CT images with clinicopathological information can facilitate the identification of patients with stage II CRC who are most likely to benefit from adjuvant chemotherapy. KEY POINTS: The effectiveness of adjuvant chemotherapy for stage II colorectal cancer remains debated. A combined model successfully identified high-risk stage II colorectal cancer patients. Shapley additive explanations enhance the interpretability of the model's predictions.
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