A Scoring System for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Quantitative Functional MRI

Abstract

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADCmin ≤ 0.95 × 10−3 mm2/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter ≥ 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADCmin, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR.

Multiparametric MRI

“…Chawla et al 84 combined median ADC and dynamic contrast-enhanced (DCE) MRI parameters and distinguished chemoradiation responders from nonresponders among patients with head and neck squamous cell carcinoma, with an AUC of 0.85. For predicting microvascular invasion of hepatocellular carcinoma, which is an adverse prognostic factor, Liao et al 85 and Wang et al 86 reported high predictive values of multiparametric MRI using multivariate logistic regression analyses. The AUC was 0.84 with the model using tumor size (≥3 cm), single tumor involving more than 2 segments, mean ADC, and minimum ADC in the study by Liao et al, 85 whereas the AUC was 0.784 by combining mean kurtosis and irregular circumferential enhancement in the study by Wang et al 86 Another machine learning study with linear discrimination analysis utilized T1, T2, and proton density values acquired with QRAPMASTER extracted from gray matter, white matter, and lesion masks 87 .…”

“…For predicting microvascular invasion of hepatocellular carcinoma, which is an adverse prognostic factor, Liao et al 85 and Wang et al 86 reported high predictive values of multiparametric MRI using multivariate logistic regression analyses. The AUC was 0.84 with the model using tumor size (≥3 cm), single tumor involving more than 2 segments, mean ADC, and minimum ADC in the study by Liao et al, 85 whereas the AUC was 0.784 by combining mean kurtosis and irregular circumferential enhancement in the study by Wang et al 86 Another machine learning study with linear discrimination analysis utilized T1, T2, and proton density values acquired with QRAPMASTER extracted from gray matter, white matter, and lesion masks. 87 They used mean values of these masks to differentiate between multiple sclerosis, a demyelinating disorder of the central nervous system, and hereditary diffuse leukoencephalopathy with spheroids, a rare demyelinating disorder that can sometimes be misdiagnosed as multiple sclerosis.…”