Qiu Zhan-Cheng, Cai Hao-Zheng, Wu You-Wei, Dai Jun-Long, Qi Wei-Li, Chen Chu-Wen, Xu Yue-Qing, Li Chuan, Wen Tian-Fu
Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
BMC Gastroenterol. 2025 Jan 12;25(1):14. doi: 10.1186/s12876-025-03588-6.
Early identification of the risk of early cancer-related death (within one year, ECRD) due to recurrence after liver resection for hepatocellular carcinoma (HCC) patients with Barcelona Clinic Liver Cancer (BCLC) stage B/C is important for surgeons to make clinical decisions. Our study aimed to establish a nomogram to predict the ECRD due to recurrence for HCC patients with BCLC stage B/C.
A total of 672 HCC patients with BCLC stages B/C from four medical centers between January 2012 and December 2018 were included in our study. The patients were randomly divided into a training cohort (n = 404) and a validation cohort (n = 268) at a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to establish a nomogram model.
In our LASSO-logistic regression model, three variables were independently associated with the ECRD due to recurrence: the alpha-fetoprotein-tumor burden score (ATS score, Odd Ratio [OR]: 1.12, p = 0.001), BCLC stage (OR: 4.39, p < 0.001) and the aspartate transaminase (AST) to alanine transaminase (ALT) ratio (AAR, OR: 1.49, p = 0.027) and we established the nomogram model based on these three variables. The nomogram model showed superior predictive ability in the training cohort (Area under the curve [AUC]: 0.754, 95%CI: 0.703-0.804) and the validation cohort (AUC: 0.741, 95%CI: 0.660-0.823). Compared with the ATS score, BCLC stage and AAR, the nomogram both had better predictive ability in both the training cohort (ATS score, AUC: 0.699, 95%CI: 0.646-0.752, p = 0.010; BCLC stage, AUC: AUC: 0.684, 95%CI: 0.637-0.732, p < 0.001; AAR, AUC: 0.593, 95%CI: 0.522-0.663, p < 0.001) and the validation cohort (ATS score, AUC: 0.659, 95%CI: 0.577-0.740, p = 0.002; BCLC stage, AUC: 0.688, 95%CI: 0.622-0.753, p = 0.009; AAR, AUC: 0.540, 95%CI: 0.436-0.645, p < 0.001).
We established a nomogram that had excellent predictive power for predicting the ECRD due to recurrence in HCC patients with BCLC stage B/C, which might help surgeons to avoid futile liver resection.
对于巴塞罗那临床肝癌（BCLC）分期为B/C期的肝细胞癌（HCC）患者，肝切除术后早期识别因复发导致的早期癌症相关死亡风险（一年内，ECRD）对于外科医生做出临床决策至关重要。我们的研究旨在建立一种列线图，以预测BCLC分期为B/C期的HCC患者因复发导致的ECRD。
2012年1月至2018年12月期间，来自四个医疗中心的672例BCLC B/C期HCC患者纳入我们的研究。患者按6:4的比例随机分为训练队列（n = 404）和验证队列（n = 268）。采用最小绝对收缩和选择算子（LASSO）逻辑回归模型建立列线图模型。
在我们的LASSO逻辑回归模型中，三个变量与因复发导致的ECRD独立相关：甲胎蛋白-肿瘤负荷评分（ATS评分，比值比[OR]：1.12，p = 0.001）、BCLC分期（OR：4.39，p < 0.001）以及天冬氨酸转氨酶（AST）与丙氨酸转氨酶（ALT）比值（AAR，OR：1.49，p = 0.027），我们基于这三个变量建立了列线图模型。列线图模型在训练队列（曲线下面积[AUC]：0.754，95%可信区间：0.703 - 0.804）和验证队列（AUC：0.741，95%可信区间：0.660 - 0.823）中显示出卓越的预测能力。与ATS评分、BCLC分期和AAR相比，列线图在训练队列（ATS评分，AUC：0.699，95%可信区间：0.646 - 0.752，p = 0.010；BCLC分期，AUC：AUC：0.684，95%可信区间：0.637 - 0.732，p < 0.001；AAR，AUC：0.593，95%可信区间：0.522 - 0.663，p < 0.001）和验证队列（ATS评分，AUC：0.659，95%可信区间：0.577 - 0.740，p = 0.002；BCLC分期，AUC：0.688，95%可信区间：0.622 - 0.753，p = 0.009；AAR，AUC：0.540，95%可信区间：0.436 - 0.645，p < 0.001）中均具有更好的预测能力。
我们建立了一种列线图，对于预测BCLC分期为B/C期的HCC患者因复发导致的ECRD具有出色的预测能力，这可能有助于外科医生避免无意义的肝切除术。
