Prediction of 131I uptake in lung metastases of differentiated thyroid cancer using deep learning.
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| Title: | Prediction of 131I uptake in lung metastases of differentiated thyroid cancer using deep learning. |
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| Authors: | Song H; Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China., Fei M; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China., Tao H; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China., Qiu Z; Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China., Shen C; Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China., Chen X; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Luo Q; Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China., She H; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China., Wang Q; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China., Zhang L; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China., Luo Q; Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. |
| Source: | Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2025 Dec 15; Vol. 16, pp. 1697233. Date of Electronic Publication: 2025 Dec 15 (Print Publication: 2025). |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101555782 Publication Model: eCollection Cited Medium: Print ISSN: 1664-2392 (Print) Linking ISSN: 16642392 NLM ISO Abbreviation: Front Endocrinol (Lausanne) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1664-2392 |
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| DOI: | 10.3389/fendo.2025.1697233 |
