分子影像学杂志2026，Vol.49Issue(8)：1051-1058,8.DOI:10.12122/j.issn.1674-4500.2026.08.10
基于ResNet101技术的多模态磁共振成像2.5D深度学习模型可有效预测肝细胞癌细胞角蛋白-19表达
A novel 2.5D deep learning model integrated with ResNet101 based on multimodal magnetic resonance imaging enables effective prediction of CK-19 expression in hepatocellular carcinoma
摘要
Abstract
Objective To develop and validate a 2.5D deep learning(DL)model integrated with the ResNet101 architecture based on multimodal MRI for the preoperative prediction of cytokeratin-19(CK-19)expression in hepatocellular carcinoma(HCC).Methods A total of 218 patients diagnosed with HCC who underwent preoperative MRI at the Third Affiliated Hospital of Soochow University from January 2018 to January 2025 were retrospectively enrolled,and randomly divided into a training cohort(TC,n=152)and a validation cohort(VC,n=66)at a ratio of 7:3.CK-19 expression was determined as either positive or negative according to immunohistochemical analysis.A 2.5D DL model was constructed on the ResNet101 framework,and quantitative imaging features were extracted from T2-weighted imaging(T2WI),arterial phase(AP),and portal venous phase(PVP)MRI sequences.The LASSO algorithm was applied to screen the most robust and discriminative imaging features,which were subsequently incorporated into a 2.5D DL model with an Extreme Gradient Boosting classifier.The predictive performance of each model for CK-19 expression was assessed using the AUC.Calibration curves and decision curve analysis(DCA)were utilized to evaluate the predictive consistency and clinical net benefit of the established models.Gradient-weighted Class Activation Mapping(Grad-CAM)was performed to visualize the intuitive heat maps within HCC lesions.Results For the single-sequence models,the T2WI-based 2.5D model achieved AUC values of 0.81(95%CI:0.74-0.88)in the TC and 0.71(95%CI:0.54-0.86)in the VC.The AP-based 2.5D model yielded AUCs of 0.83(95%CI:0.76-0.89)and 0.74(95%CI:0.69-0.84)in the TC and VC,respectively.The PVP-based 2.5D model exhibited AUC values of 0.80(95%CI:0.72-0.88)for the TC and 0.70(95%CI:0.57-0.88)for the VC.Notably,the multimodal combined 2.5D MRI model obtained the optimal predictive performance,with AUCs of 0.87(95%CI:0.80-0.93)in the TC and 0.78(95%CI:0.64-0.93)in the VC,accompanied by favorable calibration and superior clinical net benefit.Grad-CAM visualization successfully localized the focal regions corresponding to CK-19 positive expression in HCC tissues.Conclusion The ResNet101-based 2.5D model derived from multimodal MRI enables effective prediction of CK-19 expression in HCC.In particular,the integrated multimodal MRI model achieves high predictive stability and favorable clinical net benefit.关键词
磁共振成像/ResNet 101/深度学习/肝细胞癌/细胞角蛋白-19Key words
magnetic resonance imaging/ResNet 101/deep learning/hepatocellular carcinoma/cytokeratin-19引用本文复制引用
秦莉,刘海峰..基于ResNet101技术的多模态磁共振成像2.5D深度学习模型可有效预测肝细胞癌细胞角蛋白-19表达[J].分子影像学杂志,2026,49(8):1051-1058,8.基金项目
江苏省常州市科技局项目(CJ20244017) （CJ20244017）
苏州大学附属第三医院临床研究专项(2024-14) （2024-14）
苏州大学附属第三医院临床应用新技术(2026-12) （2026-12）
