Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features
Abstract To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the Fi...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08001-6 |
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| author | Yunyun Shen Renjun Huang Yinghui Zhang Jianguo Zhu Yonggang Li |
| author_facet | Yunyun Shen Renjun Huang Yinghui Zhang Jianguo Zhu Yonggang Li |
| author_sort | Yunyun Shen |
| collection | DOAJ |
| description | Abstract To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the First Affiliated Hospital of Soochow University and Jiangsu Province Hospital (2016–2023). We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. A total of 163 patients, with a median age of 53 years (range: 24–73), were divided into a training group (n = 115) and a validation group (n = 48). Among them, 54 (33.13%) had ALN metastasis, and 109 (66.87%) were non-metastasis. Nottingham grade (P = 0.005), tumor size (P = 0.016) were significant difference between non-metastasis cases and metastasis cases. In the validation set, the LR-based combined model achieved the highest AUC (0.828, 95%CI: 0.706–0.950) with excellent sensitivity (0.813) and accuracy (0.812). Although the RF-based model had the highest AUC in the training set and the highest specificity (0.906) in the validation set, its performance was less consistent compared to the LR model. MRI-T2WI radiomic features predict ALN metastasis in TNBC, with integration into clinical models enhancing preoperative predictions and personalizing management. |
| format | Article |
| id | doaj-art-cb2d8902fa264efc9c9a7b82ddb3dc88 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-cb2d8902fa264efc9c9a7b82ddb3dc882025-08-20T03:45:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-08001-6Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical featuresYunyun Shen0Renjun Huang1Yinghui Zhang2Jianguo Zhu3Yonggang Li4Department of Radiology, Suzhou Industrial Park Xinghai HospitalDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Suzhou Industrial Park Xinghai HospitalDepartment of Radiology, Suzhou Industrial Park Xinghai HospitalDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityAbstract To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the First Affiliated Hospital of Soochow University and Jiangsu Province Hospital (2016–2023). We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. A total of 163 patients, with a median age of 53 years (range: 24–73), were divided into a training group (n = 115) and a validation group (n = 48). Among them, 54 (33.13%) had ALN metastasis, and 109 (66.87%) were non-metastasis. Nottingham grade (P = 0.005), tumor size (P = 0.016) were significant difference between non-metastasis cases and metastasis cases. In the validation set, the LR-based combined model achieved the highest AUC (0.828, 95%CI: 0.706–0.950) with excellent sensitivity (0.813) and accuracy (0.812). Although the RF-based model had the highest AUC in the training set and the highest specificity (0.906) in the validation set, its performance was less consistent compared to the LR model. MRI-T2WI radiomic features predict ALN metastasis in TNBC, with integration into clinical models enhancing preoperative predictions and personalizing management.https://doi.org/10.1038/s41598-025-08001-6Triple-negative breast cancerAxillary lymph node metastasisRadiomicsMagnetic resonance imagingMachine learning, nomogramPrediction model |
| spellingShingle | Yunyun Shen Renjun Huang Yinghui Zhang Jianguo Zhu Yonggang Li Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features Scientific Reports Triple-negative breast cancer Axillary lymph node metastasis Radiomics Magnetic resonance imaging Machine learning, nomogram Prediction model |
| title | Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features |
| title_full | Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features |
| title_fullStr | Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features |
| title_full_unstemmed | Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features |
| title_short | Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features |
| title_sort | prediction of axillary lymph node metastasis in triple negative breast cancer using mri radiomics and clinical features |
| topic | Triple-negative breast cancer Axillary lymph node metastasis Radiomics Magnetic resonance imaging Machine learning, nomogram Prediction model |
| url | https://doi.org/10.1038/s41598-025-08001-6 |
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