Assessment of an MR Elastography-Based Nomogram as a Potential Imaging Biomarker for Predicting Microvascular Invasion of Hepatocellular Carcinoma

Background: Microvascular invasion (MVI) is a well-established poor prognostic factor for hepatocellular carcinoma (HCC). Preoperative prediction of MVI is important for both therapeutic and prognostic purposes, but noninvasive methods are lacking. Purpose: To develop an MR elastography (MRE)-based nomogram for the preoperative prediction of MVI in HCC. Study Type: Prospective. Subjects: A total of 111 patients with surgically resected single HCC (52 MVI-positive and 59 MVI-negative), randomly allocated to training and validation cohorts (7:3 ratio). Field Strength/Sequence: 2D-MRE and conventional sequences (T1-weighted in-phase and opposed phase gradient echo, T2-weighted fast spin echo, diffusion-weighted single-shot spin echo echo-planar, and dynamic contrast-enhanced T1-weighted gradient echo) at 3.0 T. Assessment: MRE-stiffness and conventional qualitative and quantitative MRI features were evaluated and compared between MVI-positive and MVI-negative HCCs. Statistical Tests: Univariable and multivariable logistic regression analyses were applied to identify potential predictors for MVI, and a nomogram was constructed according to the predictive model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. Harrell's C-index evaluated the discrimination performance of the nomogram, calibration curves analyzed its diagnostic performance and decision curve analysis determined its clinical usefulness. A P value < 0.05 was considered statistically significant. Results: Tumor stiffness > 6.284 kPa (odds ratio [OR] = 24.38) and the presence of arterial peritumoral enhancement (OR = 6.36) were independent variables associated with MVI. The areas under the ROC curves for tumor stiffness were 0.81 (95% confidence interval [CI]: 0.70, 0.89) and 0.77 (95% CI: 0.60, 0.90) in the training and validation cohorts, respectively. When both predictive variables were integrated, the best nomogram performance was achieved with C-indices of 0.88 (95% CI: 0.78, 0.94) and 0.87 (95% CI: 0.71, 0.96) in the two cohorts, fitting well in calibration curves. The decision curve exhibited optimal net benefit with a wide range of threshold probabilities for the nomogram. Data Conclusion: An MRE-based nomogram may be a potential noninvasive imaging biomarker for predicting MVI of HCC preoperatively.