Zhang Shu-Wen, Zhang Ning-Ning, Zhu Wen-Wen, Liu Tian, Lv Jia-Yu, Jiang Wen-Tao, Zhang Ya-Min, Song Tian-Qiang, Zhang Li, Xie Yan, Zhou Yong-He, Lu Wei
Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Department of Hepatology, Tianjin Third Central Hospital, Tianjin, China.
Front Oncol. 2022 Jul 22;12:946531. doi: 10.3389/fonc.2022.946531. eCollection 2022.
Treatments for patients with early-stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.
We retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan-Meier survival curve, and decision curve analyses (DCAs), respectively.
Prognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.
We developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.
早期肝细胞癌（HCC）患者的治疗方法包括肝移植（LT）、肝切除术（LR）、射频消融（RFA）和微波消融（MWA），这些对患者的长期生存至关重要。然而，据我们所知，尚未开发出一种能够预测HCC患者独立于治疗的预后情况的计算模型，如总生存期（OS）和无复发生存期（RFS）。本研究的目的是确定与HCC患者OS和RFS相关的预后因素，并分别建立列线图来预测它们。
我们回顾性地从中国的三家医院中检索了730例HCC患者，并在侵入性治疗后对他们进行了3年和5年的随访。所有纳入的患者分别以7:3的比例随机分为训练队列和验证队列。通过多变量Cox回归分析确定与OS和RFS相关的独立预后因素。分别构建了两个列线图预后模型，并通过一致性指数（C-index）、校准曲线、受试者操作特征曲线（ROC）下面积、ROC曲线的时间依赖性面积（AUC）、Kaplan-Meier生存曲线和决策曲线分析（DCA）进行评估。
确定了OS和RFS的预后因素，并成功构建了列线图。校准鉴别对于OS和RFS列线图预测模型都很好（C-index分别为0.750和0.746）。对于这两个列线图，AUC显示出出色的预测性能；DCA表明该模型具有良好的决策能力；校准曲线显示出强大的预测能力。列线图成功地区分了与OS和RFS相关的HCC高危和低危患者。
我们开发了列线图生存预测模型，以预测侵入性治疗后HCC的预后，在训练队列和独立测试队列中均具有可接受的准确性。这些模型在指导临床治疗策略的选择方面可能具有临床价值。
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