Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Department of Pancreatobiliary Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, P. R. China.
Cancer Med. 2023 Jun;12(12):13167-13181. doi: 10.1002/cam4.6003. Epub 2023 Apr 27.
Massive hepatocellular carcinoma (MHCC, a maximum tumor size of at least 10 cm) tends to have a poor prognosis. Therefore, this study aims to construct and validate prognostic nomograms for MHCC.
Clinic data of 1292 MHCC patients between 2010 and 2015 were got from the surveillance, epidemiology, and end results (SEER) cancer registration database. The whole set was separated into the training and validation sets at a ratio of 2:1 randomly. Variables, significantly associated with cancer-specific (CSS) and overall survival (OS) of MHCC were figured out by multivariate Cox regression analysis and were taken to develop nomograms. The concordance index (C-index), calibration curve, and decision curve analysis (DCA) were taken to validate the predictive abilities and accuracy of the nomograms.
Race, alpha-fetoprotein (AFP), grade, combined summary stage, and surgery were identified as independent factors of CSS, and fibrosis score, AFP, grade, combined summary stage, and surgery significantly correlated with OS in the training cohort. They then were taken to construct prognostic nomograms. The constructed model for predicting CSS exhibited satisfactory performance with a C-index of 0.727 (95% CI: 0.746-0.708) in the training group and 0. 672 (95% CI: 0.703-0.641) in the validation group. Besides, the model for predicting OS of MHCC also showed strong performance both in the training group (C-index: 0.722, 95% CI: 0.741-0.704) and the validation (C-index: 0.667, 95% CI: 0.696-0.638) group. All calibration curves and decision curves performed satisfactory predictive accuracy and clinic application values of the nomograms.
The web-based nomograms for CSS and OS of MHCC were developed and validated in this study, which prospectively could be tested and may serve as additional tools to assess patient's individualized prognosis and make precise therapeutic selection to improve the poor outcome of MHCC.
巨块型肝癌（MHCC，肿瘤最大径至少为 10cm）往往预后不良。因此，本研究旨在构建和验证 MHCC 的预后列线图。
从监测、流行病学和最终结果（SEER）癌症登记数据库中获取 2010 年至 2015 年间 1292 例 MHCC 患者的临床数据。将整个数据集按 2:1 的比例随机分为训练集和验证集。通过多变量 Cox 回归分析确定与 MHCC 患者癌症特异性生存（CSS）和总生存（OS）显著相关的变量，并用于开发列线图。采用一致性指数（C-index）、校准曲线和决策曲线分析（DCA）来验证列线图的预测能力和准确性。
种族、甲胎蛋白（AFP）、分级、综合分期和手术被确定为 CSS 的独立因素，纤维化评分、AFP、分级、综合分期和手术与训练队列中的 OS 显著相关。然后，它们被用来构建预后列线图。用于预测 CSS 的模型在训练组中的 C-index 为 0.727（95%CI：0.746-0.708），在验证组中的 C-index 为 0.672（95%CI：0.703-0.641），表现出良好的性能。此外，用于预测 MHCC OS 的模型在训练组（C-index：0.722，95%CI：0.741-0.704）和验证组（C-index：0.667，95%CI：0.696-0.638）中均表现出较强的性能。所有校准曲线和决策曲线均表现出良好的预测准确性和列线图的临床应用价值。
本研究构建并验证了基于网络的 MHCC CSS 和 OS 列线图，可对其进行前瞻性测试，并可能作为评估患者个体化预后的附加工具，以及进行精准治疗选择，从而改善 MHCC 的不良预后。
