Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer
Objective: To develop a nomogram based on multi-sequence MRI (msMRI) radiomics features and imaging characteristics for predicting human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC). Methods: 206 women diagnosed with invasive BC were retrospectively enrolled and randoml...
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025007789 |
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| author | Mengyi Shen Li Zhang Dingyi Zhang Xin He Nian Liu Xiaohua Huang |
| author_facet | Mengyi Shen Li Zhang Dingyi Zhang Xin He Nian Liu Xiaohua Huang |
| author_sort | Mengyi Shen |
| collection | DOAJ |
| description | Objective: To develop a nomogram based on multi-sequence MRI (msMRI) radiomics features and imaging characteristics for predicting human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC). Methods: 206 women diagnosed with invasive BC were retrospectively enrolled and randomly divided into a training set (n = 144) and a validation set (n = 62) at the ratio of 7 : 3. Tumor segmentation and feature extraction were performed on dynamic contrast-enhanced (DCE) MRI, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. Radiomics models were constructed using radiomics features and the radiomics score (Rad-score) was calculated. Rad-score and significant imaging characteristics were included in the multivariate analysis to establish the nomogram. The performance was mainly evaluated via the area under the receiver operating characteristic curve (AUC). Results: Edema types on T2WI (OR = 4.480, P = 0.008), enhancement type (OR = 7.550, P = 0.002), and Rad-score (OR = 5.906, P < 0.001) were independent imaging predictors for HER2 expression. Radiomics model based on msMRI (including DCE-MRI, T2WI, and ADC map) had AUCs of 0.936 and 0.880 in the training and validation sets, respectively, exceeding the AUCs of one sequence or dual sequences. With the combination of edema and enhancement types, the nomogram achieved the highest performance in the training set (AUC: 0.940) and validation set (AUC: 0.893). Conclusion: The developed multi-sequence MRI-based nomogram presents a promising tool for predicting HER2 expression, and is expected to improve the diagnosis and treatment of BC. |
| format | Article |
| id | doaj-art-5690e8df3d64424d9c88ea5fb487e5c0 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-5690e8df3d64424d9c88ea5fb487e5c02025-02-06T05:12:36ZengElsevierHeliyon2405-84402025-02-01113e42398Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancerMengyi Shen0Li Zhang1Dingyi Zhang2Xin He3Nian Liu4Xiaohua Huang5Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaCorresponding author.; Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaObjective: To develop a nomogram based on multi-sequence MRI (msMRI) radiomics features and imaging characteristics for predicting human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC). Methods: 206 women diagnosed with invasive BC were retrospectively enrolled and randomly divided into a training set (n = 144) and a validation set (n = 62) at the ratio of 7 : 3. Tumor segmentation and feature extraction were performed on dynamic contrast-enhanced (DCE) MRI, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. Radiomics models were constructed using radiomics features and the radiomics score (Rad-score) was calculated. Rad-score and significant imaging characteristics were included in the multivariate analysis to establish the nomogram. The performance was mainly evaluated via the area under the receiver operating characteristic curve (AUC). Results: Edema types on T2WI (OR = 4.480, P = 0.008), enhancement type (OR = 7.550, P = 0.002), and Rad-score (OR = 5.906, P < 0.001) were independent imaging predictors for HER2 expression. Radiomics model based on msMRI (including DCE-MRI, T2WI, and ADC map) had AUCs of 0.936 and 0.880 in the training and validation sets, respectively, exceeding the AUCs of one sequence or dual sequences. With the combination of edema and enhancement types, the nomogram achieved the highest performance in the training set (AUC: 0.940) and validation set (AUC: 0.893). Conclusion: The developed multi-sequence MRI-based nomogram presents a promising tool for predicting HER2 expression, and is expected to improve the diagnosis and treatment of BC.http://www.sciencedirect.com/science/article/pii/S2405844025007789Breast cancerMagnetic resonance imagingRadiomicsNomogramHuman epidermal growth factor receptor 2 |
| spellingShingle | Mengyi Shen Li Zhang Dingyi Zhang Xin He Nian Liu Xiaohua Huang Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer Heliyon Breast cancer Magnetic resonance imaging Radiomics Nomogram Human epidermal growth factor receptor 2 |
| title | Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| title_full | Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| title_fullStr | Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| title_full_unstemmed | Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| title_short | Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| title_sort | multi sequence mri based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer |
| topic | Breast cancer Magnetic resonance imaging Radiomics Nomogram Human epidermal growth factor receptor 2 |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025007789 |
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