A model incorporating clinicopathologic and liver imaging reporting and data system-based magnetic resonance imaging features to identify hepatocellular carcinoma in LR-M observations

Abstract

PURPOSETo evaluate the predictive value of a combination model of Liver Imaging Reporting and Data System (LI-RADS)-based magnetic resonance imaging (MRI) and clinicopathologic features to identify atypical hepatocellular carcinoma (HCC) in LI-RADS category M (LR-M) observations.METHODSA total of 105 patients with HCC based on surgery or biopsy who underwent preoperative MRI were retrospectively reviewed in the training group from hospital-1 between December 2016 and November 2020. The LI-RADS-based MRI features and clinicopathologic data were compared between LR-M HCC and non-HCC groups. Univariate and least absolute shrinkage and selection operator regression analyses were used to select the features. Binary logistic regression analysis was then conducted to estimate potential predictors of atypical HCC. A predictive nomogram was established based on the combination of MRI and clinicopathologic features and further validated using an independent external set of data from hospital-2.RESULTSOf 113 observations from 105 patients (mean age, 61 years; 77 men) in the training set, 47 (41.59%) were classified as LR-M HCC. Following multivariate analysis, aspartate aminotransferase >40 U/L [odds ratio (OR): 4.65], alpha-fetoprotein >20 ng/mL (OR: 13.04), surface retraction (OR: 0.16), enhancing capsule (OR: 5.24), blood products in mass (OR: 8.2), and iso/hypoenhancement on delayed phase (OR: 10.26) were found to be independently correlated with LR-M HCC. The corresponding area under the curve for a combined model-based nomogram was 0.95 in the training patients (n = 113) and 0.90 in the validation cohort (n = 53).CONCLUSIONThe combined model incorporating clinicopathologic and MRI features demonstrated a satisfactory prediction result for LR-M HCC.

Keywords

• liver imaging reporting and data system
• hepatocellular carcinoma
• lr-m
• magnetic resonance imaging
• model