摘要目的:构建基于循环肿瘤细胞(CTC)的预测肝细胞癌(HCC)患者肝切除术后超进展复发的列线图模型。方法:回顾性分析2013年1月至2022年12月在广西医科大学附属肿瘤医院肝胆外科行肝切除术治疗的231例HCC患者的临床资料，其中男性200例，女性31例，年龄46(39，52)岁。231例患者分为两组：建模组( n＝154)和验证组( n＝77)。依据术后超进展复发情况，154例建模组患者分为超复发组( n＝39)和未超复发组( n＝115)。77例验证组患者中超进展复发16例，未超进展复发61例。收集患者的CTC计数、甲胎蛋白、术后病理等临床资料。logistic回归分析术后超进展复发的影响因素，基于多因素logistic回归分析结果构建列线图模型。通过绘制受试者工作特征(ROC)曲线、校准曲线、决策曲线分析(DCA)、临床影响曲线(CIC)验证模型。 结果:多因素logistic回归分析结果表明，年龄≤45岁( OR＝6.704，95% CI：1.619～27.760， P＝0.009)、肿瘤包膜不完整( OR＝13.292，95% CI：3.084～57.295， P＝0.001)、高CTC计数( OR＝1.101，95% CI：1.023～1.186， P＝0.011)、高Ki67指数( OR＝52.659，95% CI：3.215～862.604， P＝0.005)的HCC患者，肝切除术后超进展复发的风险高。基于3个术前变量构建了列线图模型，校准曲线显示，列线图预测结果和实际结果具有良好的符合度。分别在建模组和验证组中绘制列线图模型预测HCC患者肝切除术后超进展复发的ROC曲线，曲线下面积分别为0.907(95% CI：0.856～0.959)和0.833(95% CI：0.721～0.945)。DCA表明，该列线图模型可作为有价值的预测工具预测肝切除术后超进展复发的风险。CIC结果显示，该列线图模型判断发生超进展复发的群体与实际发生超进展复发的群体高度匹配。 结论:本研究建立的基于年龄、肿瘤包膜、CTC计数的列线图模型可在术前较为准确地预测HCC患者术后超进展复发，经验证该模型具有良好的临床应用价值。

abstractsObjective:To establish a nomogram model for predicting the hyper-progression recurrence after hepatectomy in patients with hepatocellular carcinoma (HCC) based on circulating tumor cells (CTC).Methods:Clinical data of 231 HCC patients undergoing hepatectomy at the Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital from January 2013 to December 2022 were retrospectively analyzed, including 200 males and 31 females, aged 46(39, 52) years old. Patients were divided into two groups: the modeling group ( n=154) and the validation group ( n=77). According to the state of postoperative hyper-progression recurrence, patients in the modeling group were subdivided into hyper-progression recurrence ( n=39) and non-hyper-progression recurrence group ( n=115). Patients in the validation group were also subdivided into hyper-progression recurrence ( n=16) and non-hyper-progression recurrence group ( n=61). Clinicopathological data such as the total CTC count, alpha-fetoprotein, and postoperative pathology were collected. Logistic regression analysis was used to analyze the influencing factors of postoperative hyper-progression recurrence. A nomogram model was established based on the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to validate the nomogram model. Results:Multivariate logistic regression analysis showed that HCC patients with age ≤45 years old ( OR=6.704, 95% CI: 1.619-27.760, P=0.009), incomplete tumor capsule ( OR=13.292, 95% CI: 3.084-57.295, P=0.001), high total numbers of CTC ( OR=1.101, 95% CI: 1.023-1.186, P=0.011) and high Ki67 index ( OR=52.659, 95% CI: 3.215-862.604, P=0.005) had a high risk of hyper-progression recurrence after hepatectomy. The above three preoperative variables were integrated to construct a nomogram model. The calibration curve showed that the predicted results of the nomogram model were in good agreement with the actual results. The ROC curves of the nomogram model for predicting hyper-progression recurrence after hepatectomy in HCC patients were plotted, and the area under the curve was 0.907 (95% CI: 0.856-0.959) and 0.833 (95% CI: 0.721-0.945) in the modeling group and validation group, respectively. DCA showed that the nomogram model could be used as a valuable predictive tool for the hyper-progression recurrence after hepatectomy. The CIC showed that the population judged by the nomogram model was highly matched with the actual population with hyper-progression recurrence. Conclusions:This study established a nomogram model based on age, tumor capsular integrity and total CTC count, which could accurately predict the postoperative hyper-progression recurrence in HCC patients before hepatectomy. The model is promising in guiding clinical practice after further validation.