Department of Pediatric Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Updates Surg. 2024 Aug;76(4):1223-1234. doi: 10.1007/s13304-024-01814-6. Epub 2024 May 25.
For patients with hepatoblastoma (HB), current staging system is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. A retrospective analysis of 424 HB patients was performed from 2004 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to screen for variables. The identified variables were used to build survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, and receiver operating characteristic (ROC) curve. The Cox regression analysis identified six variables affecting overall survival (OS) in HB patients, including race, tumor size, lymph node involvement, distant metastases, surgery and chemotherapy. And the Cox regression analysis identified five variables including race, lymph node involvement, distant metastases, surgery, and chemotherapy that affect cancer-specific survival (CCS) in HB patients. In the training cohort, the C-index of the nomogram in predicting the OS was 0.791 [95% confidence intervals (95% CI) 0.717-0.865], CSS was 0.805(95% CI 0.728-0.882). In the validation cohort, the C-index of the nomogram in predicting the OS was 0.712 (95% CI 0.511-0.913), the CSS was 0.751 (95% CI 0.566-0.936). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1-, 3-, and 5-year OS were 0.842 (95% CI 0.739-0.944), 0.759 (95% CI 0.670-0.849), and 0.770 (95% CI 0.686-0.852), respectively. In the validation cohort, the AUC values for prediction of the 1-, 3-, and 5-year OS were 0.920 (95% CI 0.806-1.034), 0.863 (95% CI 0.750-0.976), and 0.844 (95% CI 0.721-0.967), respectively. Two nomogram models were developed and validated in this study which provided accurate prediction of the OS and CSS in HB patients. The constructed models can be used for predicting survival outcomes and guide treatment for HB patients.
对于患有肝母细胞瘤（HB）的患者，目前的分期系统在预测生存结果方面并不准确。本研究旨在开发两种准确的生存预测模型来指导临床决策。使用监测、流行病学和最终结果（SEER）数据库对 2004 年至 2015 年的 424 名 HB 患者进行了回顾性分析。使用单变量和多变量 Cox 回归分析筛选变量。使用识别出的变量构建生存预测模型。基于一致性指数（C-index）、校准图和接收器工作特征（ROC）曲线评估列线图模型的性能。Cox 回归分析确定了影响 HB 患者总体生存率（OS）的六个变量，包括种族、肿瘤大小、淋巴结受累、远处转移、手术和化疗。Cox 回归分析确定了影响 HB 患者癌症特异性生存率（CCS）的五个变量，包括种族、淋巴结受累、远处转移、手术和化疗。在训练队列中，列线图预测 OS 的 C-index 为 0.791[95%置信区间（95%CI）0.717-0.865]，CCS 为 0.805（95%CI 0.728-0.882）。在验证队列中，列线图预测 OS 的 C-index 为 0.712（95%CI 0.511-0.913），CCS 为 0.751（95%CI 0.566-0.936）。在训练队列中，列线图预测 1、3 和 5 年 OS 的受试者工作特征曲线（ROC）曲线下面积（AUC）值分别为 0.842（95%CI 0.739-0.944）、0.759（95%CI 0.670-0.849）和 0.770（95%CI 0.686-0.852）。在验证队列中，预测 1、3 和 5 年 OS 的 AUC 值分别为 0.920（95%CI 0.806-1.034）、0.863（95%CI 0.750-0.976）和 0.844（95%CI 0.721-0.967）。本研究建立并验证了两种列线图模型，为 HB 患者的 OS 和 CCS 提供了准确的预测。所构建的模型可用于预测生存结果并指导 HB 患者的治疗。
