Key result
The TabICLv2 tabular foundation model predicted acute postoperative protein depletion with a validation AUC of 0.766, demonstrating comparable discrimination to XGBoost and Random Forest.
Why the study?
Acute postoperative protein depletion frequently complicates colon cancer surgery and exacerbates adverse outcomes, yet early risk stratification remains challenging.
Does the TabICLv2 tabular foundation model accurately predict acute postoperative protein depletion in colon cancer patients?
Cohort (n=812)
No
Does the TabICLv2 tabular foundation model accurately predict acute postoperative protein depletion in colon cancer patients?
The TabICLv2 foundation model provides moderate discrimination for predicting acute postoperative protein depletion in colon cancer patients, performing comparably to traditional machine learning algorithms.
May enable early risk stratification after colon cancer surgery; leaves open prospective validation before clinical adoption.
Background: Acute postoperative protein depletion, including hypoalbuminaemia and hypoproteinaemia, frequently complicates colon cancer surgery and exacerbates adverse outcomes, yet early risk stratification remains challenging. Aims: To develop a predictive model utilising a tabular foundation model for acute postoperative protein depletion in colon cancer patients, alongside an interpretable clinical web tool. Methods: We retrospectively evaluated perioperative data from 812 colon cancer patients treated between 2020 and 2025. Following recursive feature elimination, eight traditional machine learning algorithms and the TabICLv2 tabular foundation model were trained. Discrimination, calibration, incremental risk stratification, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), paired DeLong tests, calibration curves, Brier score, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to visualise feature contributions. Results: TabICLv2 achieved the numerically highest validation AUC among the evaluated algorithms using nine selected predictors, with an AUC of 0.766 (95% CI: 0.699-0.832) and a Brier score of 0.158. DeLong tests showed no statistically significant AUC differences between TabICLv2 and XGBoost or Random Forest. NRI/IDI analyses indicated improved event reclassification across comparator models, with significant total NRI and IDI improvement over Random Forest, whereas incremental improvement over Logistic Regression and XGBoost was limited. Sensitivity analyses using albumin-only and total-protein-only endpoints showed broadly consistent discrimination. SHAP analysis revealed age, prealbumin (PA), and globulin (GLO) as the leading contributors to model predictions. A web-based calculator was subsequently deployed to facilitate clinical translation. Conclusion: TabICLv2 integrates demographic, nutritional, and immunological profiles to predict acute postoperative protein depletion with moderate discrimination. The accompanying application may provide adjunctive individualised risk assessment, but prospective multicentre validation is required before routine clinical implementation.
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Cao et al. (2026) conducted a cohort in Colon cancer (acute postoperative protein depletion) (n=812). TabICLv2 tabular foundation model vs. Traditional machine learning algorithms (e.g., XGBoost, Random Forest) was evaluated on Validation Area Under the Curve (AUC) for predicting acute postoperative protein depletion (AUC 0.766, 95% CI 0.699-0.832, p=0.854). The TabICLv2 tabular foundation model predicted acute postoperative protein depletion with a validation AUC of 0.766, demonstrating comparable discrimination to XGBoost and Random Forest.
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