Purpose: To develop and evaluate a computer-aided diagnosis (CAD) program for liver lesions on magnetic resonance (MR) images for classification of the risk of hepatocellular carcinoma (HCC) following the liver imaging reporting and data system (LI-RADS). Materials and Methods: Liver MR images from 41 patients with hyperenhancing liver lesions categorized as LR 3, 4, and 5 were evaluated by two radiologists. The major LI-RADS features of each index liver lesion were recorded, including size (maximum transverse diameter), presence of hyperenhancement, washout appearance, and capsule appearance. A CAD program was implemented to register MR images at different contrast-enhancement phases, segment liver lesions, extract lesion features, and classify lesions according to LI-RADS. The LI-RADS features quantified by CAD were compared with those assessed by radiologists using the intraclass correlation coefficient (ICC) and receiver operator curve (ROC) analyses. The LI-RADS categorization between CAD and radiologists was evaluated using the weighted Cohen's kappa coefficient. Results: The mean and standard deviation of the lesion diameters were 21 ± 11 mm (range, 7–70 mm) by radiologists and 22 ± 11 mm (range, 8–72 mm) by CAD (ICC, 0.96–0.97). The area under the curve (AUC) for the washout assessment by CAD was 0.79–0.93 with sensitivity 0.69–0.82 and specificity 0.79–1. The AUC for the capsule assessment by CAD was 0.79–0.9 with sensitivity 0.75–0.9 and specificity 0.82–0.96. The classifications by the radiologists and CAD coincided in 76–83% lesions (k = 0.57–0.71), while the agreements between radiologists were in 78% lesions (k = 0.59). Conclusion: We developed a CAD program for liver lesions on MR images and showed a substantial agreement in the LI-RADS-based classification of the risk of HCCs between the CAD and radiologists. Level of Evidence: 1. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2018;47:710–722.
- computer-aided diagnosis
- hepatocellular carcinoma
- image processing
- liver imaging reporting and data system
- quantitative imaging biomarker
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging