Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

The objective was to develop and validate an explainable artificial intelligence (AI)-based multimodal approach for preoperative risk stratification of central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) and to evaluate its role in supporting radiologist decision-making. This multicenter retrospective study enrolled patients with pathologically confirmed PTC from four hospitals. Preoperative two-dimensional ultrasound, strain elastography, shear-wave elastography, and clinical variables were integrated to develop a multimodal predictive model. Model interpretability was achieved using SHapley Additive exPlanations (SHAP) to provide feature-level explanations supporting clinical interpretation. To assess clinical usability, a controlled reader study was conducted in which six radiologists with varying experience independently evaluated cases under three conditions: without AI assistance, with basic AI assistance (probability output only), and with explainable AI assistance (visualized feature-level contributions). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and reader performance was assessed using paired statistical comparisons and interreader agreement analysis. A total of 428 patients (mean age, 44 years ± 12; 369 women) with 508 PTC nodules were included, of whom 225 (44.3%) had CLNM. The multimodal model achieved AUCs of 0.975, 0.917, and 0.844 in the training, validation, and external test cohorts, respectively, outperforming single-modality and simplified fusion approaches (p < 0.05). SHAP identified age, texture-derived radiomic features, and elastography-derived stiffness-related features as key contributors. In the reader study, explainable AI assistance significantly improved diagnostic accuracy across all experience levels, increased diagnostic confidence, and raised human-AI agreement to substantial or almost-perfect levels. An explainable AI-based multimodal approach enables accurate preoperative risk stratification of CLNM in PTC and improves radiologist diagnostic performance, with potential to support clinical decision-making within radiology workflows.