Classification tree analysis to evaluate the most useful magnetic resonance image type in the differentiation between early and progressed hepatocellular carcinoma

Aim: Using classification tree analysis, we evaluated the most useful magnetic resonance (MR) image type in the differentiation between early and progressed hepatocellular carcinoma (eHCC and pHCC).

Methods: We included pathologically proven 214 HCCs (28 eHCCs and 186 pHCCs) in 144 patients. The signal intensity of HCCs was assessed on in-phase (T1in) and opposed-phase T1-weighted images (T1op), ultrafast T2-weighted images (ufT2WI), fat-saturated T2-weighted images (fsT2WI), diffusion-weighted images (DWI), contrast enhanced T1-weighted images in the arterial phase (AP), portal venous phase (PVP), and the hepatobiliary phase. Fat content and washout were also evaluated. Fisher's exact test was performed to evaluate usefulness for the differentiation. Then, we chose MR images using binary logistic regression analysis and performed classification and regression tree analysis with them. Diagnostic performances of the classification tree were evaluated using a stratified 10-fold cross-validation method.

Results: T1in, ufT2WI, fsT2WI, DWI, AP, PVP, fat content, and washout were all useful for the differentiation (p < 0.05), and AP and T1in were finally chosen for creating classification trees (p < 0.05). AP appeared in the first node in the tree. The area under the curve, sensitivity and specificity for eHCC, and balanced accuracy of the classification tree were 0.83 (95% CI 0.74-0.91), 0.64 (18/28, 95% CI 0.46-0.82), 0.94 (174/186, 95% CI 0.90-0.97), and 0.79 (95% CI 0.70-0.87), respectively.

Conclusions: AP is the most useful MR image type and T1in the second in the differentiation between eHCC and pHCC.