Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging
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 m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62042-z |
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| author | Pengcheng Shen Zheyu Yang Jingjing Sun Yun Wang Cheng Qiu Yirou Wang Yongyong Ren Sheng Liu Wei Cai Hui Lu Siqiong Yao |
| author_facet | Pengcheng Shen Zheyu Yang Jingjing Sun Yun Wang Cheng Qiu Yirou Wang Yongyong Ren Sheng Liu Wei Cai Hui Lu Siqiong Yao |
| author_sort | Pengcheng Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1527856046fd4eebb4f3f98989a82b34 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-1527856046fd4eebb4f3f98989a82b342025-08-20T03:43:14ZengNature PortfolioNature Communications2041-17232025-08-0116111710.1038/s41467-025-62042-zExplainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imagingPengcheng Shen0Zheyu Yang1Jingjing Sun2Yun Wang3Cheng Qiu4Yirou Wang5Yongyong Ren6Sheng Liu7Wei Cai8Hui Lu9Siqiong Yao10Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDepartment of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Shanghai Fourth People’s Hospital, School of Medicine, Tongji UniversityHepatobiliary Pancreatic Center, Xuzhou Central HospitalMedical college, Nantong UniversityDepartment of Endocrinology and Metabolism, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDepartment of Thyroid and Breast Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji UniversityDepartment of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityAbstract 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.https://doi.org/10.1038/s41467-025-62042-z |
| spellingShingle | Pengcheng Shen Zheyu Yang Jingjing Sun Yun Wang Cheng Qiu Yirou Wang Yongyong Ren Sheng Liu Wei Cai Hui Lu Siqiong Yao Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging Nature Communications |
| title | Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| title_full | Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| title_fullStr | Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| title_full_unstemmed | Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| title_short | Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| title_sort | explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging |
| url | https://doi.org/10.1038/s41467-025-62042-z |
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