Semiautomated magnetic resonance imaging-based breast density measurement from routine sequences: comparison with mammography.
Authors
Affiliations (10)
Affiliations (10)
- Graduate School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan. Electronic address: [email protected].
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129, Nagaoka, Izunokushi city, Shizuoka, 410-2295, Japan. Electronic address: [email protected].
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Faculty of Health and Welfare Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa-ku, Tokyo, 116-8551, Japan. Electronic address: [email protected].
- Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawaharacho, Shogoin, Sakyo-ku Kyoto, 606-8507, Japan. Electronic address: [email protected].
- Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
- Graduate School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Department of Radiology, Fujita Health University Hospital, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
- Department of Radiology, Minoh City Hospital, 5-7-1 Kayano, Minoh-shi, Osaka 562-0014, Japan. Electronic address: [email protected].
- Graduate School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Department of Medical Imaging Technology, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
- Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan. Electronic address: [email protected].
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan. Electronic address: [email protected].
Abstract
High breast density increases breast cancer risk and decreases mammographic sensitivity, yet prior magnetic resonance imaging (MRI)-based density assessments have relied on specialized sequences or vendor-dependent software. This study aimed to evaluate a semiautomated workflow for MRI-based breast density measurement using routinely acquired MRI sequences and compare its diagnostic performance and concordance with visual and software-based mammographic classifications. In this single-center retrospective study, we analyzed 113 women (mean age: 60.8 ± 13.6 years) who underwent both breast MRI and mammography. The contralateral, normal breast was evaluated. MRI was performed using a 3.0-T scanner with a 3D T1-weighted gradient-echo sequence. Breast and fibroglandular tissues were segmented using a deep learning-assisted automated method with manual correction. Statistical analyses included correlation analysis, Bland-Altman analysis, and receiver operating characteristic (ROC) analysis. MRI-based breast density showed a strong correlation with mammographic density (r = 0.781, P < 0.001), although MRI values were on average 27.7% lower. The area under the ROC curve for detecting high-density breasts was 0.869 for MRI and 0.799 for mammography (P = 0.085). Semiautomated MRI-based breast density assessment using routine MRI sequences allowed for quantitative evaluation of breast composition without using ionizing radiation. MRI-based density measurements exhibited classification performance comparable to mammographic assessment. These findings support the potential utility of MRI for quantitative breast density evaluation, although further validation and workflow standardization are required before broader clinical application.