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Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma
Lin Wang1, Yue-Xinzi Jin1, Ya-Zhou Ji1
1Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.
Background:
Microvascular invasion (MVI) is an important prognostic factor affecting early recurrence and overall survival in hepatocellular carcinoma (HCC) patients after hepatectomy and liver transplantation, but it can be determined only in surgical specimens. Accurate preoperative prediction of MVI is conducive to clinical decisions.
Aim:
To develop and validate a preoperative prediction model for MVI in patients with HCC.
Methods:
Data from 454 patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Nanjing Medical University between May 2016 and October 2019 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Logistic regression analysis was used to identify variables significantly associated with MVI that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by using R software.
Results:
MVI was confirmed in 209 (46.0%) patients by a pathological examination. Multivariate logistic regression analysis identified four risk factors independently associated with MVI: Tumor size [odds ratio (OR) = 1.195; 95% confidence interval (CI): 1.107-1.290; P < 0.001], number of tumors (OR = 4.441; 95%CI: 2.112-9.341; P < 0.001), neutrophils (OR = 1.714; 95%CI: 1.036-2.836; P = 0.036), and serum α-fetoprotein (20-400 ng/mL, OR = 1.955; 95%CI: 1.055-3.624; P = 0.033; >400 ng/mL, OR = 3.476; 95%CI: 1.950-6.195; P < 0.001). The concordance index was 0.79 (95%CI: 0.74-0.84) and 0.81 (95%CI: 0.74-0.89) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes.
Conclusion:
We have developed and validated a preoperative prediction model for MVI in patients with HCC. The model could aid physicians in clinical treatment decision making.
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