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.