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在哈西莫托甲状腺炎中彻底改变甲状腺结节诊断:人工智能驱动的放射学和深度学习模型

Fan Wu¹, Ting Pan², Xuanwei Huang³

¹Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China.

International journal of surgery (London, England)

|December 17, 2025

概括

结合放射学和深度学习 (DL) 功能的人工智能 (AI) 模型在区分良性和恶性甲状腺结节方面表现有前途. 这种人工智能方法的诊断性能与细针吸收 (FNA) 活检相当,可能减少不必要的程序.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

在哈西莫托甲状腺炎 (HT) 中区分良性和恶性甲状腺结节 (TNs) 是临床上具有挑战性的.
目前的诊断方法可能需要具有并发症潜力的侵入性手术.

研究的目的:

评估使用放射学和深度学习 (DL) 功能用于HT的TN的AI模型的诊断效果.
用BRAFV600E测试来比较AI模型对细针吸收 (FNA) 细胞学的性能.

主要方法:

对1585名HT患者和超声波图像的分析.
使用卷积神经网络提取放射学和DL特征.
使用特征选择 (LASSO) 和机器学习开发DL-radiomics (DLR) 模型.
使用AUC,精度,灵敏度,特异性和读者研究进行性能评估.

主要成果:

DLR模型显示了高的诊断性能 (在验证时AUC高达0.917).
DLR模型的表现优于初级医生,并且显示了与FNA活检相似的结果.
SHAP和Grad-CAM方法为模型预测提供了可解释的见解.

结论:

通过SHAP和Grad-CAM增强的DLR模型提高了HT中TN的诊断准确性.
这种AI方法提供了与FNA细胞学相比的诊断效果,可能减少不必要的活检.
该模型有助于临床医生为活检和治疗做出个性化决策.

关键词:

在SHAP中,价值是SHAP值.人工智能的人工智能是人工智能.深度学习是一种深度学习.哈希莫托的甲状腺炎皮囊甲状腺癌是甲状腺癌.

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