ArticleThe British journal of radiology2025
Recurrence-free survival prediction for anal squamous cell carcinoma after chemoradiotherapy using planning CT-based radiomics model.
Article in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Tumour morphology as a visible readout of cancer biology: Towards artificial intelligence-enabled multimodal risk stratification in anal squamous cell carcinoma.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2026Article
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6 authors.
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Abstract
objectivesApproximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT.
methodsRadiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with 5 repeats of nested 5-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis.
resultsShape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher concordance index (0.80 vs 0.73) and AUC (0.84 vs 0.78 for 1-year RFS, 0.84 vs 0.79 for 2-year RFS, and 0.85 vs 0.81 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (P < .001).
conclusionsA treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only. ADVANCES IN KNOWLEDGE: The use of radiomics from planning CT is promising in assisting in personalized management in ASCC. The study outcomes support the role of planning CT-based radiomics as potential imaging biomarker.
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