区域引导解耦融合网络用于伴或不伴桥本甲状腺炎的超声甲状腺结节分类
多中心回顾性研究纳入9家机构8667例患者共13680张超声图像，开发区域引导解耦融合网络（DFNet）区分伴或不伴桥本甲状腺炎的甲状腺结节良恶性。DFNet在验证集ACC 91.9%、AUC 91.4%，测试集ACC 93.6%、AUC 92.4%，显著优于10种对比架构（p<0.05）。建模结节内至结节周围过渡并关联影像组学生物标志物，有望减少不必要的细针穿刺活检。
要点
- DFNet在伴或不伴桥本甲状腺炎的结节分类中测试集AUC达92.4%，优于10种对比架构。
- 结节周围特征使MCC最高提升12.9%，解耦融合提升6.1-9.0%，HT特异性适配提升2.9-5.4%。
- SHAP提示GLDM-LDHGLE、LBP-2D-FO-TE、OFK等影像组学标志物具有HT依赖模式。
局限性
- 回顾性研究设计。
- ROI需手动分割，流程依赖人工。
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
RATIONALE AND OBJECTIVES: Differentiating benign from malignant thyroid nodules is particularly challenging in patients with Hashimoto's thyroiditis (HT), where inflammatory changes can mimic cancer. We developed a region-guided decoupled fusion network (DFNet) that explicitly models intra- and peri-nodular transitions in both HT and non-HT nodules. By improving classification balance and interpretability, DFNet may help reduce unnecessary biopsies while preserving reliable detection of malignancy.
METHODS: In this multicenter retrospective study, 8667 patients (13,680 ultrasound images) from nine institutions were included. Nodules were confirmed histopathologically after surgery. Regions of interest (ROIs) representing intra- and peri-nodular areas were manually segmented, expanded/shrunk in fixed pixel increments, and normalized. A total of 1578 radiomic features were extracted from each ROI. DFNet employed a Swin Transformer backbone to obtain regional features, orthogonal constraint-based decomposition to separate common and region-specific representations, and HT-specific fusion before classification. Interpretability was achieved via Shapley Additive Explanations (SHAP) and correlation of deep features with radiomic descriptors. Performance was compared with 10 state-of-the-art architectures using accuracy (ACC), Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC). Statistical significance was assessed using the DeLong test and t tests with Bonferroni correction.
RESULTS: DFNet achieved the best results in validation (ACC 91.9%, MCC 76.4%, AUC 91.4%) and testing cohorts (ACC 93.6%, MCC 83.0%, AUC 92.4%), significantly outperforming alternatives (p<0.05). Peri-nodular features improved MCC by up to 12.9%, decoupled fusion by 6.1-9.0%, and HT-specific adaptation by 2.9-5.4%. SHAP highlighted biomarkers (e.g., GLDM-LDHGLE, LBP-2D-FO-TE, OFK) with HT-dependent patterns.
CONCLUSION: DFNet improves thyroid nodule classification by modeling intra- to peri-nodular transitions and linking deep features with radiomic biomarkers, enabling more accurate and interpretable predictions that may help reduce unnecessary fine-needle aspiration biopsies.
引用
- Wen J, Chen Q, Luo L, et al. Region-guided decoupled fusion network for ultrasound-based classification of thyroid nodules with and without Hashimoto's thyroiditis. Eur J Radiol. 2026. PMID: 41996838. doi:10.1016/j.ejrad.2026.112843
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。