A Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatocellular Carcinoma: Single-Center Analyses With Internal Validation

• Main Authors: Jiarui Yang, Shuguang Zhu, Juanjuan Yong, Long Xia, Xiangjun Qian, Jiawei Yang, Xueqiao Hu, Yuxuan Li, Chusi Wang, Wenguang Peng, Lei Zhang, Meihai Deng, Weidong Pan
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-03-01
• Series: Frontiers in Oncology
• Subjects: hepatocellular carcinoma; microvascular invasion; prediction model; nomogram; internal validation
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.616976/full

BackgroundMicrovascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer.MethodA total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation.ResultsLasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716–0.809) and 0.768 (0.703–0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312.ConclusionNomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.