Establishment of a nomogram model for hyper-progression recurrence after hepatectomy for hepatocellular carcinoma based on circulating tumor cells
10.3760/cma.j.cn113884-20240312-00069
- VernacularTitle:基于循环肿瘤细胞的肝细胞癌术后超进展复发列线图模型的构建
- Author:
Shuiling QIN
1
;
Jingxuan XU
;
Haowen WEI
;
Yiyue HUANG
;
Yuexiang SU
;
Haiyan LU
;
Lunan QI
Author Information
1. 广西医科大学附属肿瘤医院肝胆外科，南宁 530021
- Keywords:
Carcinoma, hepatocellular;
Circulating tumor cells;
Hyper-progression recurrence;
Nomogram
- From:
Chinese Journal of Hepatobiliary Surgery
2024;30(10):721-726
- CountryChina
- Language:Chinese
-
Abstract:
Objective: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.
