ArticleSurgery2025
American College of Surgeons survival calculator for biliary tract cancers: using machine learning to individualize predictions.
Article in Surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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
2 citing papers in PubMed.
- Identifying actionable targets: predicting imatinib nonadherence in patients with gastrointestinal stromal tumors.Journal of gastrointestinal oncology · 2026Article
- Nomogram for prediction of six-month mortality following endovascular treatment of delayed post-pancreatectomy hemorrhage.World journal of gastrointestinal surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundAlthough cancer prognosis is most commonly estimated by tumor stage, survival is multifactorial. Our objective was to develop an American College of Surgeons "Biliary Tract Cancer Survival Calculator" prototype using machine learning to generate personalized survival estimates based on patient, tumor, and treatment factors.
methodsThe National Cancer Database was used to identify all patients with biliary tract malignancies between 2010 and 2017 including intrahepatic bile duct, extrahepatic bile duct, and gallbladder cancers. Included variables were determined based on random forest algorithms and review by subject matter experts. Data were split into 80% training and 20% test data sets. Extreme gradient boosting with survival embeddings, a machine learning class, generated 3-year survival curves. Internal 5-fold cross validation was evaluated through concordance statistics (c-index), Brier scores, distant calibration, and time-dependent area under the curve.
resultsOverall, 62,877 patients were included. Metastatic disease, age at diagnosis, and lack of surgical treatment were identified as most influential on worse survival outcomes via random forest. The final model included patient (age, sex, race and ethnicity, comorbidities), tumor (clinical TNM stage, disease site, grade), and treatment (surgery, chemotherapy, radiation) factors. Accurate model discrimination, calibration, and performance was demonstrated on internal validation (c-index: 0.74, Brier score: 0.14, distant calibration: P < .001, area under the curve: 0.83). These metrics were notably improved compared to a model based solely on stage (c-index: 0.64, Brier score: 0.18, distant calibration: P < .001, time-dependent area under the curve: 0.68).
conclusionThis "Biliary Tract Cancer Survival Calculator" represents a highly accurate and comprehensive prognostic tool to estimate individualized survival estimates in real time.
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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.