Li Chunli, Wang Yuan, Bai Ruobing, Zhao Zhiyong, Li Wenjuan, Zhang Qianqian, Zhang Chaoya, Yang Wei, Liu Qi, Su Na, Lu Yueyue, Yin Xiaoli, Wang Fan, Gu Chengli, Yang Aoran, Luo Baihe, Zhou Minghui, Shen Liuhanxu, Pan Chen, Wang Zhiying, Wu Qijun, Yin Jiandong, Hou Yang, Shi Yu
Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China.
EClinicalMedicine. 2024 Oct 17;77:102881. doi: 10.1016/j.eclinm.2024.102881. eCollection 2024 Nov.
Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF.
A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n = 1208) and internal test (n = 518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs-Clinic, Image, and Fusion-were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists).
Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging ≥ F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment.
AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF.
National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).
肝纤维化（LF）的准确分期对于慢性肝病的临床管理至关重要。虽然非增强MRI（NC-MRI）可为肝脏评估提供有价值的信息，但其在预测LF方面的有效性仍未得到充分探索。本研究旨在开发并验证利用NC-MRI进行LF分期的人工智能（AI）模型。
回顾性收集了2003年10月至2022年10月在中国医科大学附属盛京医院登记的1726例患者，并将其分为开发队列（n = 1208）和内部测试队列（n = 518）。还纳入了一个由来自六个中心的337名个体组成的外部测试队列，这些个体于2015年6月至2022年11月登记。所有参与者均接受了NC-MRI（T1加权成像，T1WI；以及T2脂肪抑制成像，T2FS）和肝脏活检。使用3D上下文变压器网络在各自的图像类型上训练了两个分类模型（CMs），分别命名为T1和T2FS，并在两个测试队列上进行评估。此外，使用临床特征、T1和T2FS评分以及通过逻辑回归对它们进行整合，开发了三个CMs——临床、图像和融合模型。使用受试者操作特征曲线（AUC）下的面积评估CMs的分类有效性。在具有最高AUC的最佳模型（OMs）与其他方法（瞬时弹性成像、五种血清生物标志物和六名放射科医生）之间进行了比较。
融合模型（即OMs）在CMs中产生了最高的AUC，在内部测试队列中，显著纤维化的AUC为0.810，晚期纤维化的AUC为0.881，肝硬化的AUC为0.918，在外部测试队列中分别为0.808、0.868和0.925。OMs在AUC方面表现出卓越的性能，在LF分期方面显著超过瞬时弹性成像（仅用于分期≥F2和≥F3级）、血清生物标志物以及三名初级放射科医生。放射科医生借助OMs在LF评估中可以获得更高的AUC。
利用包括T1WI和T2FS在内的NC-MRI的AI模型能够准确地对LF进行分期。
国家自然科学基金（编号82071885）；辽宁省教育厅一般项目（LJKMZ20221160）；辽宁省科技联合计划（2023JH2/101700127）；辽宁省兴辽英才青年拔尖人才计划（XLYC2203037）。
