Region-guided decoupled fusion network for ultrasound-based classification of thyroid nodules with and without Hashimoto's thyroiditis.

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.