用多模态AI降低甲状腺结节误诊率，提高恶性风险判断准确性。
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
- 融合超声影像与分子检测数据，用注意力机制学习病变特征
- 在保持94.6%检出率基础上，阳性预测值提升至47.7%
- 适合临床减少过度手术，尤其适用于细针穿刺结果不确定者
目的：分子检测（MT）对细胞学不确定的甲状腺结节具有高敏感性但阳性预测值低，仅依赖分子特征而忽略超声影像和穿刺信息。我们通过注意力多实例学习（AMIL）方法引入超声图像，弥补这一缺陷。方法：回顾性分析加州大学洛杉矶分校医疗中心333例甲状腺结节患者（良性259例，恶性74例），构建结合超声图像与分子检测的多模态深度学习AMIL模型，用于分类并优化分子检测的恶性风险分层。结果：最终模型在保持与分子检测相同敏感性（0.946）的同时，显著提升阳性预测值（0.477对比单用分子检测的0.448），表明可减少假阳性。结论：该方法在维持高检出能力的前提下降低假阳性，有望减少因不确定结节导致的过度手术。
原文摘要 · Abstract (English)
Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy. We address this limitation by applying attention multiple instance learning (AMIL) to US images. Methods: We retrospectively reviewed 333 patients with indeterminate thyroid nodules at UCLA medical center (259 benign, 74 malignant). A multi-modal deep learning AMIL model was developed, combining US images and MT to classify the nodules as benign or malignant and enhance the malignancy risk stratification of MT. Results: The final AMIL model matched MT sensitivity (0.946) while significantly improving PPV (0.477 vs 0.448 for MT alone), indicating fewer false positives while maintaining high sensitivity. Conclusion: Our approach reduces false positives compared to MT while maintaining the same ability to identify positive cases, potentially reducing unnecessary benign thyroid resections in patients with indeterminate nodules.
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