Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer
ObjectiveThis study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.MethodsA total of 131 breast cancer patients with axillary lymph node metastasis (A...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1548888/full |
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| author | Si-Rui Wang Feng Tian Tong Zhu Chun-Li Cao Jin-Li Wang Wen-Xiao Li Jun Li Ji-Xue Hou |
| author_facet | Si-Rui Wang Feng Tian Tong Zhu Chun-Li Cao Jin-Li Wang Wen-Xiao Li Jun Li Ji-Xue Hou |
| author_sort | Si-Rui Wang |
| collection | DOAJ |
| description | ObjectiveThis study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.MethodsA total of 131 breast cancer patients with axillary lymph node metastasis (ALNM) were enrolled between June 2019 and September 2024. Patients were divided into low (n=79) and high (n=52) axillary lymph node burden (ALNB) groups. They were further split into training (n=92) and validation (n=39) cohorts. Intratumoral and peritumoral features were analyzed using the maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) methods. Six machine learning models were evaluated, and a combined clinical-radiomics model was built.ResultsThe combined logistic regression model exhibited superior diagnostic performance for high axillary lymph node burden, with areas under the ROC curve (AUC) of 0.857 in the training cohort and 0.820 in the validation cohort, outperforming individual models. The model balanced sensitivity and specificity well at a 52% cutoff value. A nomogram provided a practical risk assessment tool for clinicians.ConclusionThe combined clinical-radiomics model showed excellent predictive ability and may aid in optimizing management and treatment decisions for breast cancer patients. |
| format | Article |
| id | doaj-art-3446927f12d44ffe8f2546d7cd9249f8 |
| institution | OA Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-3446927f12d44ffe8f2546d7cd9249f82025-08-20T02:03:51ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-02-011610.3389/fendo.2025.15488881548888Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancerSi-Rui Wang0Feng Tian1Tong Zhu2Chun-Li Cao3Jin-Li Wang4Wen-Xiao Li5Jun Li6Ji-Xue Hou7The Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Neurology Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Ultrasound Diagnosis Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaThe Thyroid and Breast Surgery Department of the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, ChinaObjectiveThis study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.MethodsA total of 131 breast cancer patients with axillary lymph node metastasis (ALNM) were enrolled between June 2019 and September 2024. Patients were divided into low (n=79) and high (n=52) axillary lymph node burden (ALNB) groups. They were further split into training (n=92) and validation (n=39) cohorts. Intratumoral and peritumoral features were analyzed using the maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) methods. Six machine learning models were evaluated, and a combined clinical-radiomics model was built.ResultsThe combined logistic regression model exhibited superior diagnostic performance for high axillary lymph node burden, with areas under the ROC curve (AUC) of 0.857 in the training cohort and 0.820 in the validation cohort, outperforming individual models. The model balanced sensitivity and specificity well at a 52% cutoff value. A nomogram provided a practical risk assessment tool for clinicians.ConclusionThe combined clinical-radiomics model showed excellent predictive ability and may aid in optimizing management and treatment decisions for breast cancer patients.https://www.frontiersin.org/articles/10.3389/fendo.2025.1548888/fullbreast cancerultrasoundaxillary lymph nodes burdenradiomicsmachine learning |
| spellingShingle | Si-Rui Wang Feng Tian Tong Zhu Chun-Li Cao Jin-Li Wang Wen-Xiao Li Jun Li Ji-Xue Hou Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer Frontiers in Endocrinology breast cancer ultrasound axillary lymph nodes burden radiomics machine learning |
| title | Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| title_full | Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| title_fullStr | Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| title_full_unstemmed | Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| title_short | Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| title_sort | machine learning driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer |
| topic | breast cancer ultrasound axillary lymph nodes burden radiomics machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1548888/full |
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