Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer
Summary: Precise lymph node evaluation is fundamental to optimize CDK4/6 inhibitor therapy in luminal breast cancer, particularly given contemporary trends toward axillary surgery de-escalation that may compromise traditional lymph node staging for recurrence risk evaluation. The lymph node predicti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225011101 |
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| author | Yuhan Liu Jinlin Ye Zecheng He Mingyue Wang Changjun Wang Jie Lang Yidong Zhou Wei Zhang |
| author_facet | Yuhan Liu Jinlin Ye Zecheng He Mingyue Wang Changjun Wang Jie Lang Yidong Zhou Wei Zhang |
| author_sort | Yuhan Liu |
| collection | DOAJ |
| description | Summary: Precise lymph node evaluation is fundamental to optimize CDK4/6 inhibitor therapy in luminal breast cancer, particularly given contemporary trends toward axillary surgery de-escalation that may compromise traditional lymph node staging for recurrence risk evaluation. The lymph node prediction network (LNPN) was developed as a multi-modal model incorporating both clinicopathological parameters and ultrasonographic characteristics for lymph node burden differentiation. In a multicenter cohort of 411 patients, LNPN demonstrated robust performance, achieving an AUC of 0.92 for binary lymph node burden classification (N0 vs. N+) and 0.82 for ternary lymph node burden classification (N0/N1–3/N ≥ 4). Notably, among patients undergoing sentinel lymph node biopsy (SLNB) with confirmed 1–2 metastatic lymph nodes, LNPN predicted high-burden metastases (N ≥ 4) with an AUC of 0.77. LNPN provided a non-invasive method to assess lymph node metastasis and recurrence risk, potentially reducing unnecessary axillary lymph node dissection (ALND), and facilitating decision-making regarding the intervention of CDK4/6i in luminal breast cancer patients. |
| format | Article |
| id | doaj-art-15dbff89b06146b48aa167a320c8d0d5 |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-15dbff89b06146b48aa167a320c8d0d52025-08-20T03:26:51ZengElsevieriScience2589-00422025-07-0128711284910.1016/j.isci.2025.112849Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancerYuhan Liu0Jinlin Ye1Zecheng He2Mingyue Wang3Changjun Wang4Jie Lang5Yidong Zhou6Wei Zhang7Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Corresponding authorDepartment of Breast Surgery, Beijing Longfu Hospital, Beijing, China; Corresponding authorDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Corresponding authorSchool of Artificial Intelligence, Hebei University of Technology, Tianjin, China; Corresponding authorSummary: Precise lymph node evaluation is fundamental to optimize CDK4/6 inhibitor therapy in luminal breast cancer, particularly given contemporary trends toward axillary surgery de-escalation that may compromise traditional lymph node staging for recurrence risk evaluation. The lymph node prediction network (LNPN) was developed as a multi-modal model incorporating both clinicopathological parameters and ultrasonographic characteristics for lymph node burden differentiation. In a multicenter cohort of 411 patients, LNPN demonstrated robust performance, achieving an AUC of 0.92 for binary lymph node burden classification (N0 vs. N+) and 0.82 for ternary lymph node burden classification (N0/N1–3/N ≥ 4). Notably, among patients undergoing sentinel lymph node biopsy (SLNB) with confirmed 1–2 metastatic lymph nodes, LNPN predicted high-burden metastases (N ≥ 4) with an AUC of 0.77. LNPN provided a non-invasive method to assess lymph node metastasis and recurrence risk, potentially reducing unnecessary axillary lymph node dissection (ALND), and facilitating decision-making regarding the intervention of CDK4/6i in luminal breast cancer patients.http://www.sciencedirect.com/science/article/pii/S2589004225011101CancerMachine learning |
| spellingShingle | Yuhan Liu Jinlin Ye Zecheng He Mingyue Wang Changjun Wang Jie Lang Yidong Zhou Wei Zhang Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer iScience Cancer Machine learning |
| title | Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer |
| title_full | Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer |
| title_fullStr | Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer |
| title_full_unstemmed | Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer |
| title_short | Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer |
| title_sort | deep learning assisted non invasive lymph node burden evaluation and cdk4 6i administration in luminal breast cancer |
| topic | Cancer Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225011101 |
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