Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features
ObjectivesTo explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.MethodThe 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the t...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1546229/full |
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| author | Xinhua Li Minping Hong Zhendong Lu Zilin Liu Lifu Lin Hongfa Xu |
| author_facet | Xinhua Li Minping Hong Zhendong Lu Zilin Liu Lifu Lin Hongfa Xu |
| author_sort | Xinhua Li |
| collection | DOAJ |
| description | ObjectivesTo explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.MethodThe 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. The Cancer Imaging Archive-The Cancer Genome Atlas (TCIA-TCGA) group included 107 patients with breast cancer. A total of 1888 intratumoral and peritumoral radiomics features were extracted from DCE-MRI sequences. Radiomics models were established using a multivariate regression algorithm for each region and their combinations. Clinical and combined nomogram models integrating the Radscore with clinical risk factors were constructed. The biological significance of the radiomic features was analyzed by combining the TCIA database.ResultsThe area under the ROC curve (AUC) of radiomics model in the external validation was 0.760 (95% confidence interval [CI]: 0.626-0.874). The performance of the nomogram combined model (AUC: 0.818; 95% CI:0.702-0.916) surpassed those of both the radiomics and clinical models (AUC: 0.753; 95% CI: 0.630-0.869). Additionally, the DCA results demonstrated the usefulness of the radiomics and nomogram model.ConclusionMRI-based radiomics has the potential to predict the ALNM status in patients with invasive breast cancer. Additionally, radiogenomic analysis demonstrated a correlation between radiomic features and the immune microenvironment. |
| format | Article |
| id | doaj-art-e84d8ab3dd0645ccb29b401c0897bfe9 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-e84d8ab3dd0645ccb29b401c0897bfe92025-08-20T03:21:46ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-06-011510.3389/fonc.2025.15462291546229Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic featuresXinhua Li0Minping Hong1Zhendong Lu2Zilin Liu3Lifu Lin4Hongfa Xu5Oncology Center, Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, ChinaDepartment of Radiology, Jiaxing Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Jiaxing, Zhejiang, ChinaOncology Center, Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, ChinaOncology Center, Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, ChinaOncology Center, Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, ChinaOncology Center, Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, ChinaObjectivesTo explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.MethodThe 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. The Cancer Imaging Archive-The Cancer Genome Atlas (TCIA-TCGA) group included 107 patients with breast cancer. A total of 1888 intratumoral and peritumoral radiomics features were extracted from DCE-MRI sequences. Radiomics models were established using a multivariate regression algorithm for each region and their combinations. Clinical and combined nomogram models integrating the Radscore with clinical risk factors were constructed. The biological significance of the radiomic features was analyzed by combining the TCIA database.ResultsThe area under the ROC curve (AUC) of radiomics model in the external validation was 0.760 (95% confidence interval [CI]: 0.626-0.874). The performance of the nomogram combined model (AUC: 0.818; 95% CI:0.702-0.916) surpassed those of both the radiomics and clinical models (AUC: 0.753; 95% CI: 0.630-0.869). Additionally, the DCA results demonstrated the usefulness of the radiomics and nomogram model.ConclusionMRI-based radiomics has the potential to predict the ALNM status in patients with invasive breast cancer. Additionally, radiogenomic analysis demonstrated a correlation between radiomic features and the immune microenvironment.https://www.frontiersin.org/articles/10.3389/fonc.2025.1546229/fullaxillary lymph node metastasisradiomicsbiological significancecancer imaging archive-the cancer genome atlasbreast cancer |
| spellingShingle | Xinhua Li Minping Hong Zhendong Lu Zilin Liu Lifu Lin Hongfa Xu Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features Frontiers in Oncology axillary lymph node metastasis radiomics biological significance cancer imaging archive-the cancer genome atlas breast cancer |
| title | Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| title_full | Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| title_fullStr | Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| title_full_unstemmed | Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| title_short | Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| title_sort | radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features |
| topic | axillary lymph node metastasis radiomics biological significance cancer imaging archive-the cancer genome atlas breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1546229/full |
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