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诺模图用于术前预测肝细胞癌微血管侵袭的风险
Authors Deng G, Yao L, Zeng F, Xiao L, Wang Z
Received 17 May 2019
Accepted for publication 10 September 2019
Published 22 October 2019 Volume 2019:11 Pages 9037—9045
DOI https://doi.org/10.2147/CMAR.S216178
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Melinda Thomas
Peer reviewer comments 2
Editor who approved publication: Dr Sanjeev Srivastava
Objective: To preoperatively predict the microvascular invasion (MVI) risk in hepatocellular carcinoma (HCC) using nomogram.
Methods: A retrospective cohort of 513 patients with HCC hospitalized at Xiangya Hospital between January 2014 and December 2018 was included in the study. Univariate and multivariate analysis was performed to identify the independent risk factors for MVI. Based on the independent risk factors, nomogram was established to preoperatively predict the MVI risk in HCC. The accuracy of nomogram was evaluated by using receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).
Results: Tumor size (OR=1.17, 95% CI: 1.11–1.23, p<0.001), preoperative AFP level greater than 155 ng/mL (OR=1.65, 95% CI: 1.13–2.39, p=0.008) and NLR (OR=1.14, 95% CI: 1.00–1.29, p=0.042) were the independent risk factors for MVI. Incorporating these 3 factors, nomogram was established with the concordance index of 0.71 (95% CI, 0.66–0.75) and well-fitted calibration curves. DCA confirmed that using this nomogram added more benefit compared with the measures that treat all patients or treat none patients. At the cutoff value of predicted probability ≥0.44, the model demonstrated sensitivity of 61.64%, specificity of 71.53%, positive predictive value (PPV) of 64.13%, and negative predictive value (NPV) of 69.31%.
Conclusion: Nomogram was established for preoperative prediction of the MVI risk in HCC patients, and better therapeutic choice will be made if it was applied in clinical practice.
Keywords: microvascular invasion, MVI, hepatocellular carcinoma, HCC, preoperative prediction, independent risk factors, nomogram
