Interpretable AI-driven prediction of early postoperative recurrence in locally advanced rectal cancer using the...

Patients with locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (NCRT) and subsequently experience early recurrence (ER) within two years post-surgery tend to have unfavorable prognoses. Therefore, the accurate prediction of ER in LARC is of paramount importance.

This study aimed to develop and validate an explainable artificial intelligence (AI) model, based on the systemic inflammation-nutritional tumor biomarker index (SINTI) derived from routine blood biomarkers, to predict ER in patients with LARC.

We conducted a multicenter retrospective analysis involving two distinct patient cohorts: Cohort A (n = 715; from February 2011 to September 2017) and Cohort B (n = 224; spanning June 2020 to June 2023). Feature selection was executed utilizing the least absolute shrinkage and selection operator (LASSO) regularization to construct SINTI, effectively addressing multicollinearity. Predictive modeling incorporated ten different machine learning architectures, with hyperparameter optimization achieved through a randomized search complemented by nested tenfold cross-validation. Model performance was thoroughly evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), clinical utility curves, and calibration plots. The interpretability of the model was enhanced through SHAP value analysis, followed by its deployment as a clinical decision support web application.

The study included 715 patients from Center One and 224 from Center Two, identifying six key biomarkers as the core components of the SINTI model. Multivariable analysis confirmed that SINTI, clinical N stage, clinical T stage, and tumor size are independent predictors of early recurrence. The XGBoost algorithm exhibited robust discrimination during training cohort cross-validation, achieving a mean AUC of 0.860 (SD ± 0.021) and demonstrating consistent performance across validation datasets, with an internal AUC of 0.842 and an external AUC of 0.840. SHAP value interpretation revealed monotonic relationships between predictor variables and recurrence risk, with SINTI accounting for 36.1% of the total predictive weight. For clinical implementation, we deployed the optimized model as a web-based decision support tool, which can be accessed at https://p7toqbsdfbhlahdrugj4ra.streamlit.app/ .

This interpretable AI framework demonstrates the potential to bridge data-driven modeling and clinical decision support, offering a transparent, potentially deployable solution for post-NCRT recurrence risk prediction following further prospective validation.