Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging

Pengcheng Shen, Zheyu Yang, Jingjing Sun, Yun Wang, Cheng Qiu, Yirou Wang, Yongyong Ren, Sheng Liu (), Wei Cai (), Hui Lu () and Siqiong Yao ()
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Pengcheng Shen: Shanghai Jiao Tong University
Zheyu Yang: Shanghai Jiao Tong University School of Medicine
Jingjing Sun: Tongji University
Yun Wang: Xuzhou Central Hospital
Cheng Qiu: Nantong University
Yirou Wang: Shanghai Jiao Tong University
Yongyong Ren: Shanghai Jiao Tong University
Sheng Liu: Tongji University
Wei Cai: Shanghai Jiao Tong University School of Medicine
Hui Lu: Shanghai Jiao Tong University
Siqiong Yao: Shanghai Jiao Tong University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Preoperative prediction of lateral lymph node metastasis is clinically crucial for guiding surgical strategy and prognosis assessment, yet precise prediction methods are lacking. We therefore develop Lateral Lymph Node Metastasis Network (LLNM-Net), a bidirectional-attention deep-learning model that fuses multimodal data (preoperative ultrasound images, radiology reports, pathological findings, and demographics) from 29,615 patients and 9836 surgical cases across seven centers. Integrating nodule morphology and position with clinical text, LLNM-Net achieves an Area Under the Curve (AUC) of 0.944 and 84.7% accuracy in multicenter testing, outperforming human experts (64.3% accuracy) and surpassing previous models by 7.4%. Here we show tumors within 0.25 cm of the thyroid capsule carry >72% metastasis risk, with middle and upper lobes as high-risk regions. Leveraging location, shape, echogenicity, margins, demographics, and clinician inputs, LLNM-Net further attains an AUC of 0.983 for identifying high-risk patients. The model is thus a promising for tool for preoperative screening and risk stratification.

Date: 2025
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DOI: 10.1038/s41467-025-62042-z

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