ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025
Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
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- Artificial intelligence-based prognostic modeling of immunoradiotherapy in Barcelona clinic liver cancer stage C hepatocellular carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods.Scientific reports · 2025Article
- A nomogram-based radiomics for predicting survival to concurrent chemoradiotherapy in inoperable pancreatic cancer: a dual-center cohort study.Frontiers in immunology · 2025Article
- Machine learning-based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy.Frontiers in pharmacology · 2025Article
- Exploring HSP90α and hs-CRP using AI models to predict prognosis in advanced hepatocellular carcinoma treated with PD-1 inhibitors and targeted therapy.Frontiers in pharmacology · 2025Article
- Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods.Frontiers in oncology · 2025Article
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Abstract
backgroundThe impact of age on the causes of death (CODs) in patients with early-stage intrahepatic cholangiocarcinoma (ICC) who had undergone surgery was analyzed in this study.
methodsA total of 1555 patients (885 in the older group and 670 in the younger group) were included in this study. Before and after applying inverse probability of treatment weighting (IPTW), the different CODs in the 2 groups were further investigated. Additionally, 7 different machine learning models were used as predictive tools to identify key variables, aiming to evaluate the therapeutic outcome in early ICC patients undergoing surgery.
resultsBefore (5.92 vs. 4.08 years, P < 0.001) and after (6.00 vs. 4.08 years, P < 0.001) IPTW, the younger group consistently showed longer overall survival (OS) compared with the older group. Before IPTW, there were no significant differences in cholangiocarcinoma-related deaths (CRDs, P = 0.7) and secondary malignant neoplasms (SMNs, P = 0.78) between the 2 groups. However, the younger group had a lower cumulative incidence of cardiovascular disease (CVD, P = 0.006) and other causes (P < 0.001) compared with the older group. After IPTW, there were no differences between the 2 groups in CRDs (P = 0.2), SMNs (P = 0.7), and CVD (P = 0.1). However, the younger group had a lower cumulative incidence of other CODs compared with the older group (P < 0.001). The random forest (RF) model showed the highest C-index of 0.703. Time-dependent variable importance bar plots showed that age was the most important factor affecting the 2-, 4-, and 6-year survival, followed by stage and size.
conclusionsOur study confirmed that younger patients have longer OS compared with older patients. Further analysis of the CODs indicated that older patients are more likely to die from CVDs. The RF model demonstrated the best predictive performance and identified age as the most important factor affecting OS in early ICC patients undergoing surgery.
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