ArticleFrontiers in oncology2026
Prediction of acute postoperative protein depletion risk in colon cancer using an in-context learning foundation model: a retrospective cohort study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.