ArticleAnnals of medicine2025
A survival prediction model for biliary tract cancer based on least absolute shrinkage and selection operator-cox regression.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.Journal of gastrointestinal cancer · 2026Article
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4 authors.
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Abstract
backgroundBiliary tract cancer (BTC) is a digestive tract tumor with low incidence, high malignancy, and short survival times. Abnormal lipid metabolism may be related to the occurrence and development of tumors; therefore, we constructed a survival prediction model for patients with BTC using clinical data that included lipid indicators rarely considered in previous studies. PATIENTS AND
methodsClinical and pathological data were collected from 124 patients with BTC. Patients were divided into two groups according to the inclusion time. The training and validation cohorts included 70 patients from 2017 to 2021 and 54 patients from 2022 to 2023. Least absolute shrinkage and selection operator-Cox regression analysis was conducted on the survival data. The resulting prediction model was evaluated using discrimination and calibration analyses performed in R Studio.
resultsTumor location, lipoprotein (a), carcinoembryonic antigen, carbohydrate antigen 19-9, and therapy type were identified as key predictors for constructing the nomogram. The consistency indexes for the training and validation cohorts were 0.677 and 0.655, respectively, indicating moderate discrimination. The Hosmer-Lemeshow test provided a
conclusionsThe predictive model constructed in this retrospective study shows potential for guiding the clinical identification of groups at high risk of BTC, adjusting treatment intensity, and improving follow-up management.
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