Zeng Qin, Wang Xin, Liu Jun, Jiang Yiqing, Cao Guili, Su Ke, Liu Xiaoqin
Department of Oncology, Zigong First People's Hospital, Zigong, 643000, China.
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Discov Oncol. 2025 Apr 8;16(1):490. doi: 10.1007/s12672-025-02274-z.
This study was aimed at examining the causes of death (CODs) in patients with advanced intrahepatic cholangiocarcinoma (ICC) undergoing chemotherapy (CT). In addition, machine learning models were incorporated to predict the treatment outcomes of patients with advanced ICC and identify the factors most closely related to prognosis.
A total of 5564 patients (CT group, 3632; non-CT group, 1932) were included in the Surveillance Epidemiology and End Results registries between 2000 and 2020. The CODs were compared between the two groups before and after the inverse probability of treatment weighting (IPTW). Furthermore, seven machine learning models were utilized as predictive tools to select variable features, aiming to assess the therapeutic effectiveness in patients with advanced ICC.
After IPTW, the CT group exhibited a lower cumulative incidence of cholangiocarcinoma-related deaths (30%, 49%, and 73% vs. 59%, 66%, and 73%; P < 0.001), secondary malignant neoplasms (8.5%, 13%, and 20% vs. 19%, 22%, and 24%; P < 0.001), and other CODs (1.8%, 2.9%, and 4.4% vs. 4.1%, 4.6%, and 5.4%; P < 0.001) at 0.5-, 1-, and 3- years than the non-CT group, whereas the cumulative incidence of cardiovascular diseases (P = 0.4) was comparable between the two groups. Of the seven machine learning models, the random forest model showed the highest predictive effectiveness. This model verified that variables such as CT, radiotherapy, tumor dimensions, sex, and distant metastasis were strongly correlated with the prognosis of advanced ICC.
CT has improved the therapeutic efficacy of advanced ICC without significantly increasing other CODs. Furthermore, the analysis of various features using machine learning models has confirmed that the random forest model demonstrates the highest predictive performance.
本研究旨在探讨晚期肝内胆管癌（ICC）患者接受化疗（CT）后的死亡原因（CODs）。此外，纳入机器学习模型以预测晚期ICC患者的治疗结果，并确定与预后最密切相关的因素。
2000年至2020年期间，监测、流行病学和最终结果登记处共纳入5564例患者（CT组3632例；非CT组1932例）。在进行治疗权重逆概率（IPTW）前后，比较两组的CODs。此外，使用七个机器学习模型作为预测工具来选择变量特征，旨在评估晚期ICC患者的治疗效果。
IPTW后，CT组在0.5年、1年和3年时胆管癌相关死亡的累积发生率（分别为30%、49%和73%，对比非CT组的59%、66%和73%；P<0.001）、继发性恶性肿瘤（分别为8.5%、13%和20%，对比非CT组的19%、22%和24%；P<0.001）以及其他CODs（分别为1.8%、2.9%和4.4%，对比非CT组的4.1%、4.6%和5.4%；P< 0.001）均低于非CT组，而两组心血管疾病的累积发生率（P=0.4）相当。在七个机器学习模型中，随机森林模型显示出最高的预测有效性。该模型证实，CT、放疗、肿瘤大小、性别和远处转移等变量与晚期ICC的预后密切相关。
CT提高了晚期ICC的治疗效果，且未显著增加其他CODs。此外，使用机器学习模型对各种特征进行分析证实，随机森林模型具有最高的预测性能。
