Yang Junsheng, Bao Yongjin, Chen Weibo, Duan Yunfei, Sun Donglin
Department of Hepatopancreatobiliary Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Front Oncol. 2020 Oct 14;10:551668. doi: 10.3389/fonc.2020.551668. eCollection 2020.
Surgery is a potential cure for hepatocellular carcinoma (HCC), but its postoperative recurrence rate is high, its prognosis is poor, and reliable predictive indicators are lacking. This study was conducted to develop a simple, practical, and effective predictive model.
Preoperative clinical and postoperative pathological data on patients with HCC undergoing partial hepatectomies at the Third Affiliated Hospital of Soochow University from January 2010 to December 2015 were retrospectively analyzed, and a nomogram was constructed. The model performance was evaluated using C-indexes, receiver operating characteristic curves, and calibration curves. The results were verified from validation cohort data collected at the same center from January 2016 to January 2017 and compared with the traditional staging systems.
Three hundred three patients were enrolled in this study: 238 in the training cohort and 65 in the validation cohort. From the univariate and multivariate Cox regression analyses in the training cohort, six independent risk factors, i.e., age, alpha-fetoprotein (AFP), tumor size, satellite nodules, systemic immune inflammation index (SII), and prognostic nutritional index (PNI), were filtered and included in the nomogram. The C-index was 0.701 [95% confidence interval (CI): 0.654-0.748] in the training cohort and 0.705 (95% CI: 0.619-0.791) in the validation cohort. The areas under the curve for the 1- and 3-year recurrence-free survival were 0.706 and 0.716 in the training cohort and 0.686 and 0.743 in the validation cohort, respectively. The calibration curves showed good agreement. Compared with traditional American Joint Committee on Cancer 8th edition (AJCC8th) and Barcelona Clinic Liver Cancer (BCLC) staging systems, our nomogram showed better predictive ability.
Our nomogram is simple, practical, and reliable. According to our nomogram, predicting the risk of recurrence and stratifying HCC patient management will yield the greatest survival benefit for patients.
手术是肝细胞癌（HCC）潜在的治愈方法，但其术后复发率高、预后差且缺乏可靠的预测指标。本研究旨在建立一个简单、实用且有效的预测模型。
回顾性分析2010年1月至2015年12月在苏州大学附属第三医院接受部分肝切除术的HCC患者的术前临床和术后病理数据，并构建列线图。使用C指数、受试者工作特征曲线和校准曲线评估模型性能。结果通过2016年1月至2017年1月在同一中心收集的验证队列数据进行验证，并与传统分期系统进行比较。
本研究共纳入303例患者，其中训练队列238例，验证队列65例。通过训练队列中的单因素和多因素Cox回归分析，筛选出六个独立危险因素，即年龄、甲胎蛋白（AFP）、肿瘤大小、卫星结节、全身免疫炎症指数（SII）和预后营养指数（PNI），并纳入列线图。训练队列中的C指数为0.701[95%置信区间（CI）：0.654 - 0.748]，验证队列中的C指数为0.705（95%CI：0.619 - 0.791）。训练队列中1年和3年无复发生存率的曲线下面积分别为0.706和0.716，验证队列中分别为0.686和0.743。校准曲线显示一致性良好。与传统的美国癌症联合委员会第8版（AJCC8th）和巴塞罗那临床肝癌（BCLC）分期系统相比，我们的列线图显示出更好的预测能力。
我们的列线图简单、实用且可靠。根据我们的列线图预测复发风险并对HCC患者进行分层管理，将为患者带来最大的生存获益。
