A Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatocellular Carcinoma: Single-Center Analyses With Internal Validation - Yang, Jiarui; Zhu, Shuguang; Yong, Juanjuan; Xia, Long; Qian, Xiangjun; Yang, Jiawei; Hu, Xueqiao; Li, Yuxuan; Wang, Chusi; Peng, Wenguang; Zhang, Lei; Deng, Meihai; et al. (Frontiers in Oncology, 2021)
Background: Microvascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before operation is helpful for surgeons to make better treatment plans. Our study aims at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer. Methods: A 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. Results: Lasso 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 cutoff value of 0.312. Conclusion: Nomogram was established for predicting risk of MVI. Better treatments plans can be formulated according to the predictive results.