基于神经网络的B超分析预测MASLD患者纤维化风险
回顾性分析205例MASLD患者共636幅超声图像，用临床参数和深度学习预测构建逻辑回归模型，以同日肝硬度测量（LSM）<8 kPa为低纤维化风险、≥8 kPa为风险增高进行分类。结合临床参数和深度学习预测的模型AUROC为0.73、准确率68%，加入FIB-4后AUROC升至0.92、准确率88%，仅基于图像的模型诊断性能有限。
要点
- B超本身对纤维化相关信号弱，单独图像模型诊断性能有限。
- 临床参数联合深度学习预测AUROC仅0.73，加入FIB-4提升至0.92。
- 深度学习预测在临床参数基础上仅带来有限增量价值。
局限性
- 回顾性研究设计。
- 样本量有限（205例）。
- 深度学习预测增量价值有限，需前瞻性验证临床效用。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
OBJECTIVE: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, underscoring the need for tools to stratify individual risk of disease progression. We evaluated whether logistic regression models augmented by deep learning-based predictions (DLPs) can improve the B-mode ultrasound-based identification of at-risk MASLD, defined as patients with increased fibrosis risk.
METHODS: We retrospectively analyzed 205 patients with a total of 636 ultrasound images. We developed a model that reproduces the LSM-based dichotomous fibrosis risk classification using clinical parameters and ultrasound image-derived deep learning pipelines. Patients were classified by same-day liver stiffness measurement (LSM) (<8 kPa: low fibrosis risk; ≥8 kPa: increased fibrosis risk). We assessed the incremental value of DLPs when added to the parameters sex, age, BMI, diabetes mellitus type 2 status and the fibrosis-4 score (FIB-4) based on accuracy, AUROC, and related statistics.
RESULTS: The logistic regression model combining the clinical parameters and the DLPs achieved acceptable performance with an AUROC of 0.73 and a test accuracy of 68%. The same model without DLPs showed an AUROC of 0.72 and a test accuracy of 61%. Including FIB-4 improved performance further (AUROC 0.92, accuracy 88%). Models based solely on image data demonstrated limited diagnostic performance.
CONCLUSION: B-mode ultrasound provides a weak fibrosis-related signal, yielding limited diagnostic performance. Meaningful discrimination required the incorporation of clinical parameters, with FIB-4 offering the greatest improvement among the parameters assessed. Deep learning predictions added only modest incremental value. Prospective validation is needed to clarify clinical utility.
引用
- Stansch J, Trung KV, Blank V, et al. Analysis of B-Scan Ultrasonography Using Neural Networks to Predict Risk of Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Ultrasound Med Biol. 2026. PMID: 42080324. doi:10.1016/j.ultrasmedbio.2026.03.007
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。