Chen Chao, Chu Xiaoyuan, Liu Hong, Zhou Mingzhen, Shi Zhan, Si Anfeng, Zhao Ying, Liu Xiufeng, Shen Jie, Liu Baorui
Comprehensive Cancer Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Department of Oncology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Hepatobiliary Surg Nutr. 2024 Oct 1;13(5):771-787. doi: 10.21037/hbsn-23-646. Epub 2024 May 28.
Hepatocellular carcinoma (HCC) persists as a dominant cause of cancer-related mortality globally, with a notably rapid escalation in mortality rates. The advent of immunotherapy, particularly immune checkpoint inhibitors (ICIs), has ushered in a new era in the management of liver cancer, albeit with unresolved challenges in the context of treatment beyond progression (TBP) and stratified prognosis in diverse populations. This study aimed to develop and validate a novel nomogram model to identify factors that predict the benefit of continued immunotherapy for hepatocellular carcinoma patients following disease progression in clinical practice.
This study retrospectively analyzed the efficacy of ICIs in TBP, focusing on the Chinese population with advanced liver cancer. A nomogram was constructed based on four independent risk factors identified through Cox multivariate analysis, aiming to predict patient prognosis post-ICI treatment. The model was validated through receiver operating characteristic (ROC) curve analysis and categorized patients into high-, intermediate-, and low-risk groups, with further validation using calibration plots and decision curve analysis (DCA).
The low-risk group demonstrated significantly enhanced overall survival (OS) compared to the high-risk group, with the nomogram predictions aligning closely with actual outcomes for 6- and 9-month OS. The model exhibited commendable predictive accuracy, achieving a C-index exceeding 0.7 in both training and validation datasets. The DCA underscored the clinical utility of the nomogram-based prognostic model, further substantiated by the area under the ROC curve (AUC).
The developed nomogram presents a potentially valuable tool for predicting the prognosis of HCC patients undergoing ICI therapy beyond progression, particularly within the Chinese demographic. However, the study is constrained by its retrospective, single-center nature and necessitates further validation through large-scale, multicenter clinical studies across varied populations.
肝细胞癌(HCC)仍然是全球癌症相关死亡的主要原因,死亡率显著快速上升。免疫疗法的出现,尤其是免疫检查点抑制剂(ICIs),开创了肝癌治疗的新时代,尽管在疾病进展后治疗(TBP)和不同人群分层预后方面仍存在未解决的挑战。本研究旨在开发并验证一种新型列线图模型,以识别在临床实践中预测肝细胞癌患者疾病进展后继续免疫治疗获益的因素。
本研究回顾性分析了ICIs在TBP中的疗效,重点关注中国晚期肝癌患者。基于通过Cox多变量分析确定的四个独立危险因素构建列线图,旨在预测ICI治疗后患者的预后。通过受试者工作特征(ROC)曲线分析对模型进行验证,并将患者分为高、中、低风险组,使用校准图和决策曲线分析(DCA)进行进一步验证。
与高风险组相比,低风险组的总生存期(OS)显著延长,列线图对6个月和9个月OS的预测与实际结果密切相符。该模型表现出值得称赞的预测准确性,在训练集和验证集中的C指数均超过0.7。DCA强调了基于列线图的预后模型的临床实用性,ROC曲线下面积(AUC)进一步证实了这一点。
所开发的列线图为预测接受ICI治疗后疾病进展的HCC患者的预后提供了一种潜在有价值的工具,尤其是在中国人群中。然而,该研究受其回顾性、单中心性质的限制,需要通过跨不同人群的大规模、多中心临床研究进行进一步验证。