Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Fuxue Road, Wenzhou, Zhejiang, 325035, People's Republic of China.
Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People's Republic of China.
Updates Surg. 2022 Oct;74(5):1589-1599. doi: 10.1007/s13304-022-01308-3. Epub 2022 Jun 17.
Fibrolamellar hepatocellular carcinoma (FLC) is a rare subtype of hepatocellular carcinoma. Our study aimed to construct a nomogram to predict the cancer-specific survival (CSS) of FLC. Data of 200 FLC patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were divided into the training group and the validation group. Prognostic factors identified in the univariate and multivariate Cox regression analyses were used to construct the nomogram. The concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. As a result, age ≥ 59, N1 stage, M1 stage, tumor size ≤ 2.0 cm, and no surgery were significantly associated with lower CSS in multivariate Cox regression analysis. The calibration plot showed good consistency of the nomogram between predicted and observed outcomes in the training and validation groups. Compared with the TNM staging system, the prognostic evaluation model (PEM) showed a higher C-index (0.823 vs 0.656). The PEM also showed better predictive performance, with areas under the curve of 0.909 and 0.890 for predicting the 1- and 5-year survival. The AUCs of the TNM stage model for predicting 1- and 5-year survival were 0.629 and 0.787, respectively. In addition, the DCA curve showed that the nomogram had better clinical utility. Finally, we concluded that Age, N stage, M stage, tumor size, and surgery are independent prognostic factors for FLC. PEM established based on these five prognostic indicators can help predict the CSS of patients with FLC.
纤维板层肝细胞癌（FLC）是一种罕见的肝细胞癌亚型。本研究旨在构建一个列线图来预测 FLC 的癌症特异性生存（CSS）。我们将纳入 SEER 数据库的 200 例 FLC 患者的数据分为训练组和验证组。在单因素和多因素 Cox 回归分析中确定的预后因素用于构建列线图。使用一致性指数（C-index）、校准曲线、时间依赖性接受者操作特征曲线（ROC）和决策曲线分析（DCA）来评估列线图的性能。结果显示，年龄≥59 岁、N1 期、M1 期、肿瘤大小≤2.0cm 和无手术与多因素 Cox 回归分析中较低的 CSS 显著相关。校准图显示训练组和验证组的预测和观察结果之间的列线图具有良好的一致性。与 TNM 分期系统相比，预后评估模型（PEM）的 C-index 更高（0.823 比 0.656）。PEM 还显示出更好的预测性能，预测 1 年和 5 年生存率的曲线下面积分别为 0.909 和 0.890。TNM 分期模型预测 1 年和 5 年生存率的 AUC 分别为 0.629 和 0.787。此外，DCA 曲线显示列线图具有更好的临床实用性。总之，年龄、N 期、M 期、肿瘤大小和手术是 FLC 的独立预后因素。基于这五个预后指标建立的 PEM 可以帮助预测 FLC 患者的 CSS。
