Shen Zhehan, Chen Lingzhi, Wang Lilong, Dong Shunjie, Wang Fakai, Pan Yaning, Zhou Jiahao, Wang Yikun, Xu Xinxin, Chong Huanhuan, Lin Huimin, Li Weixia, Li Ruokun, Ma Haihong, Ma Jing, Yu Yixing, Du Lianjun, Wang Xiaosong, Zhang Shaoting, Yan Fuhua
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin 2nd Rd, Huangpu District, Shanghai 200025, China.
Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Radiol Artif Intell. 2025 Nov;7(6):e240531. doi: 10.1148/ryai.240531.
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, < .001), reduced reading time (average 34.5 seconds decrease, < .001), and enhanced confidence (average 0.3-point increase, < .001) of junior radiologists. Conclusion The proposed deep learning model accurately detected and classified FLLs, improving diagnostic accuracy and efficiency of junior radiologists. Liver, MR-Dynamic Contrast Enhanced, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Feature Detection, Vision, Application Domain © RSNA, 2025 See also commentary by Adams and Bressem in this issue.
目的 评估一种利用多参数MRI特征开发的可解释深度学习模型在提高放射科医生对肝脏局灶性病变（FLL）分类的诊断准确性和效率方面的有效性。材料与方法 纳入多参数MRI检查中直径1 cm或更大的FLL患者。使用上海交通大学医学院附属瑞金医院（2018年1月至2023年8月）的回顾性数据开发nn-Unet和肝脏影像特征Transformer模型。nnU-Net用于病变分割，肝脏影像特征Transformer模型用于FLL分类。对新疆生产建设兵团医院、苏州大学附属第一医院和新瑞医院（2018年1月至2023年12月）的数据进行外部测试，并于2024年1月至4月获得前瞻性测试集。将模型性能与放射科医生进行比较，并评估模型辅助对初级和高级放射科医生性能的影响。评估指标包括Dice相似系数和准确率。结果 共有2131例FLL患者（平均年龄56岁±12[标准差]；1476例女性患者）纳入训练集、内部测试集、外部测试集和前瞻性测试集。三个测试集中肝脏和肿瘤分割的平均Dice相似系数值分别为0.98和0.96。三个测试集中特征和病变分类的平均准确率分别为93%和97%。肝脏影像特征Transformer模型辅助阅片提高了初级放射科医生的诊断准确性（平均提高5.3%，P<0.001），缩短了阅片时间（平均减少34.5秒，P<0.001），并增强了信心（平均提高0.3分，P<0.
