Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis
Abstract Objective This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance. Methods A systematic search of PubMed, Embase, Coc...
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| Language: | English |
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BMC
2025-06-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-025-03874-3 |
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| author | Jupeng Zhang Qi Wu Peng Lei Xiqi Zhu Baosheng Li |
| author_facet | Jupeng Zhang Qi Wu Peng Lei Xiqi Zhu Baosheng Li |
| author_sort | Jupeng Zhang |
| collection | DOAJ |
| description | Abstract Objective This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance. Methods A systematic search of PubMed, Embase, Cochrane Library, Scopus, and Web of Science identified 12 eligible studies (from 3,739 records) up to August 2024. Data were extracted to calculate sensitivity, specificity, and area under the curve (AUC) using bivariate models in R 4.4.1. Study quality was assessed via QUADAS-2. Results Pooled sensitivity and specificity were 0.86 (95% CI: 0.82–0.90) and 0.82 (95% CI: 0.78–0.86), respectively, with an overall AUC of 0.90 (95% CI: 0.85–0.90). Diagnostic odds ratio (DOR) was 39.11 (95% CI: 25.04–53.17). Support vector machine (SVM) classifiers outperformed Naive Bayes, with higher sensitivity (0.88 vs. 0.86) and specificity (0.82 vs. 0.78). Heterogeneity was primarily attributed to MRI equipment (P = 0.037). Conclusion ML-based MRI models demonstrate high diagnostic accuracy for breast cancer classification, with pooled sensitivity of 0.86 (95% CI: 0.82–0.90), specificity of 0.82 (95% CI: 0.78–0.86), and AUC of 0.90 (95% CI: 0.85–0.90). These results support their clinical utility as screening and diagnostic adjuncts, while highlighting the need for standardized protocols to improve generalizability. |
| format | Article |
| id | doaj-art-313cb80668514e9ca08dc1787e5cb809 |
| institution | Kabale University |
| issn | 1477-7819 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-313cb80668514e9ca08dc1787e5cb8092025-08-20T03:45:11ZengBMCWorld Journal of Surgical Oncology1477-78192025-06-0123111110.1186/s12957-025-03874-3Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysisJupeng Zhang0Qi Wu1Peng Lei2Xiqi Zhu3Baosheng Li4Department of Radiology, Affiliated Hospital of Youjiang Medical university for NationalitiesDepartment of Radiology, Affiliated Hospital of Youjiang Medical university for NationalitiesDepartment of Radiology, Affiliated Hospital of Youjiang Medical university for NationalitiesDepartment of Radiology, Affiliated Hospital of Youjiang Medical university for NationalitiesDepartment of Radiology, Affiliated Hospital of Youjiang Medical university for NationalitiesAbstract Objective This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance. Methods A systematic search of PubMed, Embase, Cochrane Library, Scopus, and Web of Science identified 12 eligible studies (from 3,739 records) up to August 2024. Data were extracted to calculate sensitivity, specificity, and area under the curve (AUC) using bivariate models in R 4.4.1. Study quality was assessed via QUADAS-2. Results Pooled sensitivity and specificity were 0.86 (95% CI: 0.82–0.90) and 0.82 (95% CI: 0.78–0.86), respectively, with an overall AUC of 0.90 (95% CI: 0.85–0.90). Diagnostic odds ratio (DOR) was 39.11 (95% CI: 25.04–53.17). Support vector machine (SVM) classifiers outperformed Naive Bayes, with higher sensitivity (0.88 vs. 0.86) and specificity (0.82 vs. 0.78). Heterogeneity was primarily attributed to MRI equipment (P = 0.037). Conclusion ML-based MRI models demonstrate high diagnostic accuracy for breast cancer classification, with pooled sensitivity of 0.86 (95% CI: 0.82–0.90), specificity of 0.82 (95% CI: 0.78–0.86), and AUC of 0.90 (95% CI: 0.85–0.90). These results support their clinical utility as screening and diagnostic adjuncts, while highlighting the need for standardized protocols to improve generalizability.https://doi.org/10.1186/s12957-025-03874-3Machine learningDiagnostic accuracyMeta-analysisBreast cancerMagnetic resonance imaging |
| spellingShingle | Jupeng Zhang Qi Wu Peng Lei Xiqi Zhu Baosheng Li Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis World Journal of Surgical Oncology Machine learning Diagnostic accuracy Meta-analysis Breast cancer Magnetic resonance imaging |
| title | Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis |
| title_full | Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis |
| title_fullStr | Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis |
| title_full_unstemmed | Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis |
| title_short | Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis |
| title_sort | diagnostic accuracy of machine learning based magnetic resonance imaging models in breast cancer classification a systematic review and meta analysis |
| topic | Machine learning Diagnostic accuracy Meta-analysis Breast cancer Magnetic resonance imaging |
| url | https://doi.org/10.1186/s12957-025-03874-3 |
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