Background: MVI is a potential screening indicator for liver cancer liver transplantation prognosis and neoadjuvant therapy. Currently, there is no effective way to accurately predict MVI before surgery.
Objective: To construct a preoperative MVI model for liver cancer patients undergoing liver transplantation.
Methods: We retrospectively analyzed the clinical and follow-up data of 168 patients with hepatocellular carcinoma treated in the Department of General Surgery, Huashan Hospital, Fudan University from June 25, 2014 to October 31, 2018. (Randomly divided into experimental group 112 cases, verified Group of 56 patients). Inclusion criteria: pathological diagnosis of hepatocellular carcinoma (HCC); exclusion criteria: (1) major blood vessel invasion; (2) extrahepatic lymph node metastasis; (3) distant metastasis; (4) death within one month after surgery. We used Logistic regression model to analyze the risk factors of MVI, and constructed a preoperative risk prediction model of MVI. The area under the receiver curve (ROC) was used to test the predictive power of the risk model, and a calibration analysis was used to test the accuracy of the predictive model. Finally, K-M analysis was used to analyze the relationship between high and low MVI risk scores and prognosis.
Results: Two factors (number of tumors and cumulative tumor volume) were found to be independent risk factors for predicting MVI by multivariate logistic regression analysis. Based on this, they were included in the nomogram analysis to establish the number of tumors (Number)-cumulative volume (Cumulative Volume) index (NCV index). The area under the NCV index of the experimental group and the validation cohort reached 0.933 (95% CI: 0.875-0.992) and 0.861 (95% CI: 0.733-0.988), respectively, which are significantly better than the similar models reported in the literature (P <0.05). The prediction consistency of the NCV model reached 0.949 and 0.252 in the experimental group and the validation group, respectively. The optimal threshold for NCV index according to Yoden index is 0.81. The accuracy of NCV index prediction is as follows: experimental group: sensitivity 89%, specificity 90%, positive predictive value 96.1%, negative predictive value 75%; validation group: sensitivity 79.5%, specificity 91.7%, positive predictive value 97.2%, negative The predicted value is 55%. K-M analysis showed that MVI score was significantly correlated with recurrence and survival after liver cancer liver transplantation (P <0.05).
Conclusion: The NCV index based on the number of tumors and the cumulative volume can effectively predict the MVI of liver cancer patients undergoing liver transplantation and is expected to become an important evaluation tool for clinical treatment decision-making.