An interpretable deep learning method for early hepatocellular carcinoma diagnosis on multiphase CT

Early diagnosis of hepatocellular carcinoma is critical to preserve access to curative treatment options. The Liver Imaging Reporting and Data System (LI-RADS) provides a standardized framework for non-invasive diagnosis on multiphase computed tomography, based on major visual imaging features. However, these features may be difficult to assess consistently, particularly in small liver lesions. We developed dLI-RADS, an interpretable artificial intelligence method designed to estimate the probability of hepatocellular carcinoma from multiphase computed tomography images of manually segmented liver lesions, while remaining aligned with LI-RADS-related imaging concepts. dLI-RADS combines neural network-derived predictions with handcrafted quantitative imaging measurements reflecting LI-RADS major features and integrates these inputs using logistic regression. In this multicenter retrospective study, patients with multiphase computed tomography and at least one liver lesion were included, while lesions larger than 40 mm were excluded. Histology served as the reference standard for hepatocellular carcinoma, and a composite reference standard was used for non-hepatocellular carcinoma lesions. The lesion-level accuracy of dLI-RADS and LI-RADS were compared on the internal evaluation set using 10,000-repetition bootstrap non-inferiority tests, with 5% margins and significance levels. On the external test set, sensitivities were similarly compared. A total of 143 (61 years ± 9, 125 men), 62 (61y ± 10, 49 men), and 133 (59y ± 10, 103 men) patients with 196, 84, and 171 lesions (mean diameter 17.7, 18.3, and 21.8 millimeters) were respectively included in the training, internal and external evaluation sets. dLI-RADS was non-inferior to LI-RADS for lesion-level accuracy in the internal evaluation set, achieving 80% accuracy compared with 74% for LI-RADS ( p = 0.04). On the external evaluation set, dLI-RADS achieved a sensitivity of 93% compared with 78% for LI-RADS (non-inferiority p < 0.001). On this same set, dLI-RADS also achieved 74% specificity, 86% accuracy, and an AUC of 84%. Because LI-RADS features were unavailable for non-HCC lesions, only sensitivity could be directly compared between dLI-RADS and LI-RADS. These findings suggest that dLI-RADS may provide interpretable decision support for hepatocellular carcinoma diagnosis on multiphase computed tomography, particularly for small liver lesions.