An Explainable Deep Learning Model for Focal Liver Lesion Diagnosis Using Multiparametric MRI

Purpose To assess the effectiveness of an explainable deep learning model, developed using multiparametric MRI features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs 1 cm or larger in diameter at multiparametric MRI were included in the study. The nn-Unet and Liver Imaging Feature Transformer models were developed using retrospective data from the Ruijin Hospital (January 2018-August 2023). The nnU-Net was used for lesion segmentation and the Liver Imaging Feature Transformer model for FLL classification. External testing was performed on data from the Xinjiang Production and Construction Corps Hospital, the First Affiliated Hospital of Soochow University, and Xinrui Hospital (January 2018-December 2023), with a prospective test set obtained from January to April 2024. Model performance was compared with radiologists, and impact of model assistance on junior and senior radiologist performance was assessed. Evaluation metrics included the Dice similarity coefficient and accuracy. Results A total of 2131 individuals with FLLs (mean age, 56 years ± 12 [SD]; 1476 female patients) were included in the training, internal test, external test, and prospective test sets. Average Dice similarity coefficient values for liver and tumor segmentation across the three test sets were 0.98 and 0.96, respectively. Average accuracy for features and lesion classification across the three test sets were 93% and 97%, respectively. Readings assisted by the Liver Imaging Feature Transformer model improved diagnostic accuracy (average 5.3% increase, <i>P</i> < .001), reduced reading time (average 34.5 seconds decrease, <i>P</i> < .001), and enhanced confidence (average 0.3-point increase, <i>P</i> < .001) of junior radiologists. Conclusion The proposed deep learning model accurately detected and classified FLLs, improving diagnostic accuracy and efficiency of junior radiologists. <b>Keywords:</b> Liver, MR-Dynamic Contrast Enhanced, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Feature Detection, Vision, Application Domain <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Adams and Bressem in this issue.