Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.
Authors
Affiliations (8)
Affiliations (8)
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China.
- Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China.
- Department of Systems Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX, 77030, USA.
- Department of Radiology, Weill Cornell Medical College, New York, NY, 10065, USA.
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei , 230022, China. [email protected].
- Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China. [email protected].
- Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China. [email protected].
Abstract
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.