Introduction: Multidisciplinary tumor boards (MTBs) are the standard for oncologic decision-making but require substantial time and personnel resources. Large language models (LLMs) may support MTBs by generating structured, guideline-based recommendations. We evaluated concordance between MTB decisions and LLM recommendations in newly diagnosed cholangiocellular adenocarcinoma (CCA) and hepatocellular carcinoma (HCC). Method: In this retrospective single-center study, MTB protocols from 2022–2023 were screened. After exclusions, 50 CCA and 50 HCC cases were analyzed. Institutional MTB recommendations were compared with outputs from ChatGPT and Claude using identical structured clinical summaries available at the time of MTB presentation. Agreement was assessed using Cohen’s kappa, and correlation using Spearman’s rank coefficient. Results: For CCA, exact concordance between MTB and ChatGPT was 80%, with substantial agreement (k = 0.688) and strong correlation (r = 0.725; both p < 0.001). Concordance with Claude was at 56%. For HCC, concordance between MTB and ChatGPT was 66%, with substantial agreement (k = 0.604) and moderate correlation (r = 0.484; both p < 0.001). Concordance with Claude was only 38%, with fair agreement (k = 0.314, p < 0.001) and weak correlation (r = 0.086, p = 0.551). Conclusions: LLM-derived recommendations varied markedly by model. ChatGPT demonstrated substantial concordance, whereas Claude showed limited agreement. MTBs appeared more individualized, while LLM outputs were more guideline-oriented. These findings highlight the potential role of LLMs as supportive tools for structured clinical reasoning and guideline adherence. Prospective multicenter studies should evaluate real-time LLM integration into MTB workflows, efficiency, decision quality, and patient outcomes safely.

Background: Cholangiocarcinoma (CCA) of the liver is a highly aggressive cancer that arises from malignant cells in the bile ducts. Radical surgery remains the only curative option, but major liver resection carries high perioperative risks. This study investigates the predictive value of preoperative bone mineral density (BMD), measured via CT, for perioperative complications, mortality, and long-term outcomes. Methods: The analysis included 202 patients who underwent curative-intent surgery for intrahepatic cholangiocarcinoma (iCCA; n = 97) or perihilar cholangiocarcinoma (pCCA; n = 105) between 2010 and 2019. Preoperative bone mineral density (BMD) was assessed using computed tomography segmentation at the level of the 12th thoracic vertebra. Osteopenia was defined according to established cutoffs. Results: Osteopenia was highly prevalent in both iCCA (53/97, 54%) and pCCA (54/105, 51%) subcohorts. Patients suffering from osteopenia were significantly older than those without (71.1 [62–76.6] years vs. 61.3 [52.9–69.2] years; p < 0.001). Alteration in BMD did not demonstrate a significant prognostic effect in terms of perioperative morbidity (Mann–Whitney U; comprehensive complication index—CCI: 34 [9–56] vs. 40 [21–72] p = 0.185; iCCA: p = 0.803; pCCA: p = 0.165). The median overall survival in our cohort was 19 [14–25] months. Patients with osteopenia did not exhibit a significantly different overall survival compared to those with normal bone mineral density (log-rank p = 0.234). Conclusions: In contrast to our previous observations in other oncological patient cohorts, osteopenia alone had no significant negative impact on clinical outcomes in our large European cohort of patients undergoing curative-intent surgery for CCA. To validate these findings, further prospective studies are warranted.