Development and validation of a nomogram model for preoperative prediction of hepatocellular carcinoma with microvascular invasion
10.3760/cma.j.cn113884-20230120-00015
- VernacularTitle:术前预测肝细胞癌伴微血管侵犯风险列线图模型的建立与验证
- Author:
Kangkang WAN
1
;
Shubo PAN
;
Liangping NI
;
Qiru XIONG
;
Shengxue XIE
;
Longsheng WANG
;
Tao LIU
;
Haonan SUN
;
Ju MA
;
Huimin WANG
;
Zongfan YU
Author Information
1. 安徽医科大学第二附属医院普外科，合肥 230000
- Keywords:
Carcinoma, hepatocellular;
Computed tomography;
Microvascular invasion;
Nomogram
- From:
Chinese Journal of Hepatobiliary Surgery
2023;29(8):561-566
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate a nomogram model for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on preoperative enhanced computed tomography imaging features and clinical data.Methods:The clinical data of 210 patients with HCC undergoing surgery in the Second Affiliated Hospital of Anhui Medical University from May 2018 to May 2022 were retrospectively analyzed, including 172 males and 38 females, aged (59±10) years old. Patients were randomly divided into the training group ( n=147) and validation group ( n=63) by systematic sampling at a ratio of 7∶3. Preoperative enhanced computed tomography imaging features and clinical data of the patients were collected. Logistic regression was conducted to analyze the risk factors for HCC with MVI, and a nomogram model containing the risk factors was established and validated. The diagnostic efficacy of predicting MVI status in patients with HCC was assessed by receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) of the subjects in the training and validation groups. Results:The results of multifactorial analysis showed that alpha fetoprotein ≥400 μg/ml, intra-tumor necrosis, tumor length diameter ≥3 cm, unclear tumor border, and subfoci around the tumor were independent risk factors predicting MVI in HCC. A nomogram model was established based on the above factors, in which the area under the curve (AUC) of ROC were 0.866 (95% CI: 0.807-0.924) and 0.834 (95% CI: 0.729-0.939) in the training and validation groups, respectively. The DCA results showed that the predictive model thresholds when the net return is >0 ranging from 7% to 93% and 12% to 87% in the training and validation groups, respectively. The CIC results showed that the group of patients with predictive MVI by the nomogram model are highly matched with the group of patients with confirmed MVI. Conclusion:The nomogram model based on the imaging features and clinical data could predict the MVI in HCC patients prior to surgery.
