ArticleCancer management and research2026
Development and Internal Validation of a LASSO-Based Prediction Model for Colorectal Adenoma Recurrence After Polypectomy.
Article in Cancer management and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Background: Colorectal adenoma recurrence after polypectomy remains an important clinical concern, with current surveillance strategies based primarily on index adenoma characteristics. This study aimed to develop and validate a prediction model integrating clinical and metabolic factors for personalized recurrence risk assessment. Methods: We conducted a retrospective cohort study of 328 patients with colorectal adenomas confirmed by pathology between January 2018 and December 2021. Variable selection was performed using LASSO-penalized Cox regression with 10-fold cross-validation. Model performance was assessed through bootstrap validation (1000 resamples) with calculation of Harrell's C-index, calibration curves, and decision curve analysis. Results: The final model included age (HR 1.28, 95% CI 1.12-1.47), alcohol consumption history (HR 2.12, 95% CI 1.68-2.67), and bile acid levels (mean 3.14 ± 0.85 μmol/L in the recurrence group vs 2.73 ± 0.78 μmol/L in the non-recurrence group). The model demonstrated good discrimination (bootstrap-corrected C-index 0.878, 95% CI 0.843-0.912) and calibration (slope 0.894, 95% CI 0.851-0.937). Conclusion: The developed prediction model integrating age, alcohol history, and bile acid levels provides a practical tool for stratifying recurrence risk after polypectomy, with potential to guide personalized surveillance strategies. External validation is warranted to confirm these findings.
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