DeepFAN用Transformer提升肺结节良恶性判断，帮新手医生诊断更准。
DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial
- 基于Transformer融合全局与局部特征，从超1万例病理确诊结节中训练。
- 临床试验中辅助医生诊断准确率提升10.9%，敏感性提高7.6%。
- 适合需提升肺结节判读能力的放射科新手，可减少误诊漏诊。
CT广泛应用导致肺结节检出率上升。当前深度学习方法常无法全面融合全局与局部特征，且缺乏临床验证。为此，我们开发了DeepFAN，一种基于Transformer的模型，在超过1万例病理确诊结节上训练，并开展多读者、多病例临床试验，评估其对初级放射科医生的辅助效果。DeepFAN在内部测试集上诊断AUC达0.939（95% CI 0.930-0.948），在包含400例来自三家独立医疗机构的临床试验数据集上达到0.954（95% CI 0.934-0.973）。可解释性分析显示全局特征贡献更高。12名读者平均表现显著提升：AUC提高10.9%（95% CI 8.3%-13.5%），准确率提升10.0%（95% CI 8.9%-11.1%），敏感性提升7.6%（95% CI 6.1%-9.2%），特异性提升12.6%（95% CI 10.9%-14.3%）（P<0.001）。结节级别阅片者间一致性从“一般”提升至“中等”（总体kappa值：0.313 vs. 0.421；P=0.019）。结论：DeepFAN有效辅助初级放射科医生，有助于统一诊断质量，减少不确定肺结节的过度随访。中国临床试验注册中心：ChiCTR2400084624。
原文摘要 · Abstract (English)
The widespread adoption of CT has notably increased the number of detected lung nodules. However, current deep learning methods for classifying benign and malignant nodules often fail to comprehensively integrate global and local features, and most of them have not been validated through clinical trials. To address this, we developed DeepFAN, a transformer-based model trained on over 10K pathology-confirmed nodules and further conducted a multi-reader, multi-case clinical trial to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on the clinical trial dataset involving 400 cases across three independent medical institutions. Explainability analysis indicated higher contributions from global than local features. Twelve readers' average performance significantly improved by 10.9% (95% CI 8.3%-13.5%) in AUC, 10.0% (95% CI 8.9%-11.1%) in accuracy, 7.6% (95% CI 6.1%-9.2%) in sensitivity, and 12.6% (95% CI 10.9%-14.3%) in specificity (P<0.001 for all). Nodule-level inter-reader diagnostic consistency improved from fair to moderate (overall k: 0.313 vs. 0.421; P=0.019). In conclusion, DeepFAN effectively assisted junior radiologists and may help homogenize diagnostic quality and reduce unnecessary follow-up of indeterminate pulmonary nodules. Chinese Clinical Trial Registry: ChiCTR2400084624.
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