用于MASLD风险分层的超声图像学习流程的开发与评估
Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification
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中文总结 AI 辅助
本研究开发并评估了基于B型和SWE图像的超声图像学习流程，发现SWE图像学习在MASLD纤维化分期中表现优于B型图像学习，为MASLD风险分层提供了新的潜在方法。
中文摘要 AI 辅助
代谢功能障碍相关脂肪性肝病（MASLD）影响约30%的普通人群。包括B型成像和剪切波弹性成像（SWE）在内的超声成像被广泛用于无创纤维化评估，但基于深度学习的超声图像学习在MASLD风险分层中的作用仍未得到充分阐明。本研究开发并评估了使用B型和SWE图像的超声图像学习流程，用于纤维化分期和识别存在风险的代谢功能障碍相关脂肪性肝炎（MASH）患者。研究共纳入250例超声检查，每例受试者1次检查。采用3折交叉验证和受试者工作特征曲线下面积（AUROC）评估模型性能。端到端SWE图像学习在各纤维化分期上的表现与操作者引导的SWE相当。总体而言，基于SWE的学习在纤维化分期上始终优于B型图像学习，对于F≥2（显著纤维化，p=0.11），AUROC从0.64（95%置信区间：[0.56, 0.72]）提升至0.72（95%置信区间：[0.65, 0.79]）；对于F≥3（晚期纤维化，p=0.02），AUROC从0.67（95%置信区间：[0.58, 0.75]）提升至0.78（95%置信区间：[0.72, 0.85]）；对于F4（肝硬化，p=0.10），AUROC从0.69（95%置信区间：[0.56, 0.82]）提升至0.80（95%置信区间：[0.72, 0.89]）。这些发现凸显了SWE图像学习在MASLD风险分层中的潜力。
英文摘要
Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, including B-mode imaging and shear wave elastography (SWE), is widely used for noninvasive fibrosis assessment; however, the role of deep learning-based ultrasound image learning for MASLD risk stratification remains insufficiently characterized. In this study, we developed and evaluated ultrasound image learning pipelines using B-mode and SWE images for fibrosis staging and identification of patients with at-risk metabolic dysfunction-associated steatohepatitis (MASH). A total of 250 ultrasound examinations, one exam per subject, were included. Model performance was evaluated using 3-fold cross-validation with area under the receiver operating characteristic curve (AUROC). End-to-end SWE image learning achieved performance comparable to operator-guided SWE across fibrosis stages. Overall, SWE-based learning consistently outperformed B-mode image learning in fibrosis staging, with AUROC improvements from 0.64 (95%CI: [0.56, 0.72]) to 0.72 (95% CI: [0.65, 0.79]) for F>=2 (significant fibrosis, p=0.11), from 0.67 (95%CI: [0.58, 0.75]) to 0.78 (95% CI:[0.72, 0.85]) for F>=3 (advanced fibrosis, p=0.02), and from 0.69 (95%CI: [0.56, 0.82]) to 0.80 (95%CI: [0.72, 0.89]) for F4 (cirrhosis, p=0.10). These findings highlight the potential of SWE image learning for MASLD risk stratification.
发表机构
- Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
- Lincoln Laboratory, Massachusetts Institute of Technology（麻省理工学院林肯实验室）
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