Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients
Xianwei Yang,1 Jing Li,1 Hang Sun,2 Jing Chen,1 Jin Xie,1 Yonghui Peng,1 Tao Shang,1 Tongyong Pan1 1Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China; 2School of Information Science and Engineering, Shenyang Ligong University, She...
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Dove Medical Press
2025-02-01
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| Series: | Breast Cancer: Targets and Therapy |
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| Online Access: | https://www.dovepress.com/radiomics-integration-of-mammography-and-dce-mri-for-predicting-molecu-peer-reviewed-fulltext-article-BCTT |
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| author | Yang X Li J Sun H Chen J Xie J Peng Y Shang T Pan T |
| author_facet | Yang X Li J Sun H Chen J Xie J Peng Y Shang T Pan T |
| author_sort | Yang X |
| collection | DOAJ |
| description | Xianwei Yang,1 Jing Li,1 Hang Sun,2 Jing Chen,1 Jin Xie,1 Yonghui Peng,1 Tao Shang,1 Tongyong Pan1 1Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China; 2School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, People’s Republic of ChinaCorrespondence: Jing Li, Email doclijing0828@126.com; Hang Sun, Email sunhang84@126.comBackground: Accurate identification of the molecular subtypes of breast cancer is essential for effective treatment selection and prognosis prediction.Aim: This study aimed to evaluate the diagnostic performance of a radiomics model, which integrates breast mammography and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the molecular subtypes of breast cancer.Methods: We retrospectively included 462 female patients with pathologically confirmed breast cancer, including 53 cases of triple-negative, 94 cases of HER2 overexpression, 95 cases of luminal A, and 215 cases of luminal B breast cancer. Radiomics analysis was performed using FAE software, wherein the radiomic features were examined about the hormone receptor status. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy.Results: In multivariate analysis, radiomic features were the only independent predictive factors for molecular subtypes. The model that incorporates multimodal fusion features from breast mammography and DCE-MRI images exhibited superior overall performance compared to using either modality independently. The AUC values (or accuracies) for six pairings were as follows: 0.648 (0.627) for luminal A vs luminal B, 0.819 (0.793) for luminal A vs HER2 overexpression, 0.725 (0.696) for luminal A vs triple-negative subtype, 0.644 (0.560) for luminal B vs HER2 overexpression, 0.625 (0.636) for luminal B vs triple-negative subtype, and 0.598 (0.500) for triple-negative subtype vs HER2 overexpression.Conclusion: The radionics model utilizing multimodal fusion features from breast mammography combined with DCE-MRI images showed high performance in distinguishing molecular subtypes of breast cancer. It is of significance to accurately predict prognosis and determine treatment strategy of breast cancer by molecular classification.Keywords: breast cancer, molecular subtypes, magnetic resonance, mammography, radiomics |
| format | Article |
| id | doaj-art-260dfc520db94758929d230e63202b81 |
| institution | DOAJ |
| issn | 1179-1314 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Breast Cancer: Targets and Therapy |
| spelling | doaj-art-260dfc520db94758929d230e63202b812025-08-20T02:48:42ZengDove Medical PressBreast Cancer: Targets and Therapy1179-13142025-02-01Volume 17187200100294Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer PatientsYang XLi JSun HChen JXie JPeng YShang TPan TXianwei Yang,1 Jing Li,1 Hang Sun,2 Jing Chen,1 Jin Xie,1 Yonghui Peng,1 Tao Shang,1 Tongyong Pan1 1Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China; 2School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, People’s Republic of ChinaCorrespondence: Jing Li, Email doclijing0828@126.com; Hang Sun, Email sunhang84@126.comBackground: Accurate identification of the molecular subtypes of breast cancer is essential for effective treatment selection and prognosis prediction.Aim: This study aimed to evaluate the diagnostic performance of a radiomics model, which integrates breast mammography and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the molecular subtypes of breast cancer.Methods: We retrospectively included 462 female patients with pathologically confirmed breast cancer, including 53 cases of triple-negative, 94 cases of HER2 overexpression, 95 cases of luminal A, and 215 cases of luminal B breast cancer. Radiomics analysis was performed using FAE software, wherein the radiomic features were examined about the hormone receptor status. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy.Results: In multivariate analysis, radiomic features were the only independent predictive factors for molecular subtypes. The model that incorporates multimodal fusion features from breast mammography and DCE-MRI images exhibited superior overall performance compared to using either modality independently. The AUC values (or accuracies) for six pairings were as follows: 0.648 (0.627) for luminal A vs luminal B, 0.819 (0.793) for luminal A vs HER2 overexpression, 0.725 (0.696) for luminal A vs triple-negative subtype, 0.644 (0.560) for luminal B vs HER2 overexpression, 0.625 (0.636) for luminal B vs triple-negative subtype, and 0.598 (0.500) for triple-negative subtype vs HER2 overexpression.Conclusion: The radionics model utilizing multimodal fusion features from breast mammography combined with DCE-MRI images showed high performance in distinguishing molecular subtypes of breast cancer. It is of significance to accurately predict prognosis and determine treatment strategy of breast cancer by molecular classification.Keywords: breast cancer, molecular subtypes, magnetic resonance, mammography, radiomicshttps://www.dovepress.com/radiomics-integration-of-mammography-and-dce-mri-for-predicting-molecu-peer-reviewed-fulltext-article-BCTTbreast cancer · molecular subtypes · magnetic resonance · mammography · radiomics |
| spellingShingle | Yang X Li J Sun H Chen J Xie J Peng Y Shang T Pan T Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients Breast Cancer: Targets and Therapy breast cancer · molecular subtypes · magnetic resonance · mammography · radiomics |
| title | Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients |
| title_full | Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients |
| title_fullStr | Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients |
| title_full_unstemmed | Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients |
| title_short | Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients |
| title_sort | radiomics integration of mammography and dce mri for predicting molecular subtypes in breast cancer patients |
| topic | breast cancer · molecular subtypes · magnetic resonance · mammography · radiomics |
| url | https://www.dovepress.com/radiomics-integration-of-mammography-and-dce-mri-for-predicting-molecu-peer-reviewed-fulltext-article-BCTT |
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