IDDF2020-ABS-0069 Prediction of microvascular invasion before surgery in patients with hepatocellular carcinoma: a nomogram model based on inflammatory markers. (18th November 2020) - Zeng, Qianwen
Abstract : Background: Microvascular invasion (MVI) remains a risk factor for tumor recurrence and metastasis in hepatocellular carcinoma (HCC). No effective and well-recognized method can detect MVI before surgery. Inflammatory markers reflect the immune environment and have been proven to be related to prognosis as well as the presence of MVI in HCC. We aimed to establish an MVI predictive model based on inflammatory markers. Methods: Data of 1058 cases of HCC patients treated in the First Affiliated Hospital of Sun Yat-sen University from November 2003 to December 2015 were collected. In a ratio of 7 : 3, patients were divided into the training group (740 cases) and the validating group (318 cases). Inflammatory factors related to MVI diagnosis in HCC patients were selected by LASSO regression analysis, and were then integrated into an 'Inflammatory Score'. A prognostic Nomogram model was established by combining the Inflammatory Score and the independent factors determined by multivariate logistic regression analysis. The consistency index (C-index) and the area under the curve (AUC) were used to evaluate the predictive efficacy of the model. Results: A total of 1058 HCC patients were included in this retrospective study, 430 of whom (40.6%) were diagnosed with MVI. Sixteen inflammatory factors, including neutrophil, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, etc., were selected by LASSO regression analysis to establish an Inflammatory Score. Multivariate logistic regression analysis showed that Inflammatory Score (OR = 2.186, 97.5% CI: 1.656–2.950), age (OR = 0.987, 97.5% CI: 0.973–1.000), alpha fetoprotein (OR = 1.923, 97.5% CI: 1.380–2.690), tumor size (OR = 2.308, 97.5% CI: 1.656–3.220) were independent factors in the diagnosis of MVI in HCC patients. These four factors were then used to establish a Nomogram for MVI prediction. The C-index of the Nomogram prediction model was 0.72. The AUC for the training and validating group were 0.720 and 0.721, respectively. Conclusions: The Nomogram prediction model drawn in this study has a high prognostic value, which is capable of improving the diagnosis efficiency of MVI in HCC patients.