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

Authors

Shen P,Yang Z,Sun J,Wang Y,Qiu C,Wang Y,Ren Y,Liu S,Cai W,Lu H,Yao S

Affiliations (14)

  • Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China.
  • Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
  • Department of Ultrasound, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, PR China.
  • Hepatobiliary Pancreatic Center, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, PR China.
  • Medical college, Nantong University, Nantong, Jiangsu, PR China.
  • Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, PR China.
  • SJTU-Yale Joint Center of Biostatistics and Data Science, Technical Center for Digital Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, PR China.
  • Department of Thyroid and Breast Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, PR China. [email protected].
  • Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China. [email protected].
  • Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. [email protected].
  • SJTU-Yale Joint Center of Biostatistics and Data Science, Technical Center for Digital Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, PR China. [email protected].
  • Institute of Bioinformatics, Shanghai Academy of Experimental Medicine, Shanghai, PR China. [email protected].
  • Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. [email protected].
  • SJTU-Yale Joint Center of Biostatistics and Data Science, Technical Center for Digital Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, PR China. [email protected].

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.

Topics

Deep LearningThyroid NeoplasmsLymphatic MetastasisLymph NodesJournal ArticleMulticenter Study

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