Objective : Pretreatment evaluation of tumor biology and microenvironment are important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) to predict tumor behaviors, including microvascular invasion (MVI), tumor differentiation, and immunoscore (IS).
Methods : We included 273 patients with hepatocellular carcinoma (HCC) who underwent preoperative Gd-EOB-DTPA-enhanced MRI prior to hepatectomy. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariate and multivariate logistic regression analyses were performed to investigate clinical variables and qualitative and quantitative MRI features’ associations with MVI, tumor differentiation, and IS. Nomograms were developed based on significant imaging and clinical features associated with these three histopathological features in the training cohort, then validated, and evaluated.
Results : Significant predictors of MVI included tumor size (P = 0.002), rim enhancement (P = 0.017), percent reduction in T1 images (T1D%; P = 0.043), and standard deviation (SD) of apparent diffusion coefficient (P = 0.028), while capsule (P = 0.007), mean relaxation time on the hepatocellular phase (T1E; P < 0.001), and alpha-fetoprotein (AFP) levels (P = 0.003) predicted tumor differentiation. Significant predictors of IS included the radiologic score derived from the combination of tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1D%, relaxation time on plain scan (T1P), T1E (P = 0.001), and serum AFP (P = 0.027) and alanine aminotransferase levels (P = 0.028). Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699) and IS (0.737, 0.726) in the training and validation cohorts, respectively.
Conclusions : MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction, and pretreatment decisions.