A Novel Diagnostic Algorithm for Subcentimeter Hepatocellular Carcinoma Utilizing Gd-EOB-DTPA-Enhanced MRI:...

To develop and validate a novel decision-tree algorithm using Gd-EOB-DTPA-enhanced MRI for diagnosing subcentimeter hepatocellular carcinoma (scHCC ≤1.0 cm), addressing limitations of current guidelines in detecting lesions lacking arterial phase hyperenhancement (APHE).

This multicenter retrospective diagnostic accuracy study analyzed 419 patients with focal liver nodules ≤ 1.0 cm, utilizing training set (n=225) and internal test set (n=96) from a single center, and an external validation set (n=98) from two independent centers. Multivariable logistic regression and classification and regression tree (CART) modeling integrated four MRI features: restricted diffusion, non-rim APHE, transitional phase hypointensity, and mild-moderate T2WI hyperintensity. Performance was compared to LI-RADS LR-4, modified LR-4, and Japan Society of Hepatology (JSH) criteria.

A total of 419 patients (mean age 53±12 years, 333 men) were analyzed. The algorithm achieved: Training set: Sensitivity 92.9% (104/112), specificity 94.7% (107/113), accuracy 93.8%. Internal test set: Sensitivity 90.0% (36/40), specificity 91.1% (51/56), accuracy 90.6%. External validation: Sensitivity 93.3% (40/43), specificity 89.1% (49/55), accuracy 90.8%. It significantly outperformed existing criteria (P < 0.001 vs. LR-4; P = 0.002 vs. modified LR-4; P = 0.005 vs. JSH). A critical finding was that 20%-23.3% of scHCCs demonstrated the absence of APHE, yet these cases achieved diagnostic accuracy rates of 80.0%-87.5%.

This algorithm reduces dependency on APHE, integrates high-yield MRI features, and streamlines protocols. Its high accuracy across multicenter cohorts supports immediate clinical translation for early HCC detection.