Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics.
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| Title: | Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics. |
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| Authors: | Cao M; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China., Liu X; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China., Yang A; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China., Xu Y; Department of Radiology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China., Zhang Q; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China., Cao Y; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China. Electronic address: caoyt18@lzu.edu.cn. |
| Source: | Magnetic resonance imaging [Magn Reson Imaging] 2025 Oct; Vol. 122, pp. 110434. Date of Electronic Publication: 2025 Jun 01. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 8214883 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5894 (Electronic) Linking ISSN: 0730725X NLM ISO Abbreviation: Magn Reson Imaging Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2008->: Amsterdam : Elsevier Original Publication: New York : Pergamon, c1982- |
| MeSH Terms: | Breast Neoplasms*/diagnostic imaging , Breast Neoplasms*/metabolism , Receptor, ErbB-2*/metabolism , Magnetic Resonance Imaging*/methods , Multiparametric Magnetic Resonance Imaging*/methods, Humans ; Female ; Retrospective Studies ; Middle Aged ; Adult ; Aged ; Contrast Media ; Breast/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; ROC Curve ; Reproducibility of Results ; Image Processing, Computer-Assisted ; Radiomics |
| Abstract: | Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare. Background: This study aims to explore the value of multiparametric magnetic resonance imaging (MRI) techniques-dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted fat-suppressed imaging (T2WI)-in predicting human epidermal growth factor receptor 2 (HER-2) status in breast cancer by integrating intratumoral and peritumoral radiomics features to establish a multiparametric MRI intratumoral and peritumoral radiomics model. Methods: A retrospective cohort of 266 female breast cancer patients was analyzed. Patients from Center 1 (n = 199) were divided into a training set (n = 140) and internal validation set (n = 59; 7:3 ratio), while Center 2 (n = 67) provided the external test set. Using 3D Slicer, tumor boundaries were manually segmented on T2WI, DWI, and DCE-MRI to define intratumoral volumes of interest (VOIs). These VOIs were expanded by 3 mm to generate peritumoral regions (VOI_Peri3mm). Radiomics features were extracted from both regions, optimized via feature selection, and used to train eight random forest (RF) models. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The multiparametric MRI intratumoral and peritumoral radiomics model (DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF) demonstrated optimal HER-2 prediction, achieving area under the curve (AUC) values of 0.822 (95 % CI:0.755-0.889), 0.823 (0.714-0.932), and 0.813 (0.712-0.914) in the training, internal validation, and external test sets, respectively. It significantly outperformed single-parameter or single-region models and maintained cross-cohort consistency. Conclusion: The intratumoral-peritumoral radiomics fusion model integrating DWI, T2WI, and DCE-MRI provides high diagnostic accuracy for HER-2 assessment, offering non-invasive biomarkers and enhancing precision in breast cancer management. (Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Contributed Indexing: | Keywords: Breast cancer; Combined intratumoral and peritumoral regions; Multi-parameter MRI; Radiomics |
| Substance Nomenclature: | EC 2.7.10.1 (Receptor, ErbB-2) EC 2.7.10.1 (ERBB2 protein, human) 0 (Contrast Media) |
| Entry Date(s): | Date Created: 20250603 Date Completed: 20250806 Latest Revision: 20250806 |
| Update Code: | 20250807 |
| DOI: | 10.1016/j.mri.2025.110434 |
| PMID: | 40460947 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare.<br />Background: This study aims to explore the value of multiparametric magnetic resonance imaging (MRI) techniques-dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted fat-suppressed imaging (T2WI)-in predicting human epidermal growth factor receptor 2 (HER-2) status in breast cancer by integrating intratumoral and peritumoral radiomics features to establish a multiparametric MRI intratumoral and peritumoral radiomics model.<br />Methods: A retrospective cohort of 266 female breast cancer patients was analyzed. Patients from Center 1 (n = 199) were divided into a training set (n = 140) and internal validation set (n = 59; 7:3 ratio), while Center 2 (n = 67) provided the external test set. Using 3D Slicer, tumor boundaries were manually segmented on T2WI, DWI, and DCE-MRI to define intratumoral volumes of interest (VOIs). These VOIs were expanded by 3 mm to generate peritumoral regions (VOI_Peri3mm). Radiomics features were extracted from both regions, optimized via feature selection, and used to train eight random forest (RF) models. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).<br />Results: The multiparametric MRI intratumoral and peritumoral radiomics model (DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF) demonstrated optimal HER-2 prediction, achieving area under the curve (AUC) values of 0.822 (95 % CI:0.755-0.889), 0.823 (0.714-0.932), and 0.813 (0.712-0.914) in the training, internal validation, and external test sets, respectively. It significantly outperformed single-parameter or single-region models and maintained cross-cohort consistency.<br />Conclusion: The intratumoral-peritumoral radiomics fusion model integrating DWI, T2WI, and DCE-MRI provides high diagnostic accuracy for HER-2 assessment, offering non-invasive biomarkers and enhancing precision in breast cancer management.<br /> (Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.) |
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| ISSN: | 1873-5894 |
| DOI: | 10.1016/j.mri.2025.110434 |