AUTHOR=Li Shumin , Zeng Qianwen , Liang Ruiming , Long Jianyan , Liu Yihao , Xiao Han , Sun Kaiyu 

TITLE=Using Systemic Inflammatory Markers to Predict Microvascular Invasion Before Surgery in Patients With Hepatocellular Carcinoma

JOURNAL=Frontiers in Surgery

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.833779

DOI=10.3389/fsurg.2022.833779

ISSN=2296-875X

ABSTRACT=Background: Mounting studies reveal the relationship between inflammatory markers and post-therapy prognosis, yet the role of the systemic inflammatory indices in preoperative microvascular invasion (MVI) prediction for hepatocellular carcinoma (HCC) remains unclear. 
Patients and Methods: In this study, data of 1058 cases of HCC patients treated in the First Affiliated Hospital of Sun Yat-sen University from February 2002 to May 2018 were collected. Inflammatory factors related to MVI diagnosis in HCC patients were selected by least absolute shrinkage and selection operator (LASSO) regression analysis, and were then integrated into an “Inflammatory Score”. A prognostic nomogram model was established by combining the inflammatory score and other independent factors determined by multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficacy of the model.
Results: Sixteen inflammatory factors, including 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=2.14, 95% CI: 1.63-2.88, P<0.001), alpha fetoprotein (OR=2.02, 95% CI: 1.46-2.82, P<0.001), and tumor size (OR=2.37, 95% CI: 1.70-3.30, P<0.001) were independent factors for MVI. These three factors were then used to establish a nomogram for MVI prediction. The AUC for the training and validation group was 0.72 (95% CI: 0.68-0.76) and 0.72 (95% CI: 0.66-0.78), respectively.
Conclusion: These findings indicated that the model drawn in this study has a high predictive value, which is capable to assist the diagnosis of MVI in HCC patients.