Volumetric phase-specific three-dimensional radiomics and machine learning for differentiation of non-mass enhancement in breast magnetic resonance imaging.
Authors
Affiliations (5)
Affiliations (5)
- University of Health Sciences Türkiye, İzmir Tepecik Education and Research Hospital, Clinic of Radiology, İzmir, Türkiye.
- Dokuz Eylül University Faculty of Medicine, Department of Radiology, İzmir, Türkiye.
- Acıbadem University, Faculty of Medicine, Department of Bioinformatics and Biostatistics, İstanbul, Türkiye.
- Dokuz Eylül University Faculty of Medicine, Department of General Surgery, İzmir, Türkiye.
- Dokuz Eylül University Faculty of Medicine, Department of Pathology, İzmir, Türkiye.
Abstract
Non-mass enhancement (NME) in breast magnetic resonance imaging (MRI) is a diagnostically challenging entity due to overlapping benign and malignant features, observer variability, and high false-positive rates. This study evaluated the diagnostic performance of three-dimensional (3D) volumetric radiomics and multiple machine learning (ML) algorithms, using early (2<sup>nd</sup>) and late (7<sup>th</sup>) post-contrast phases to differentiate benign from malignant NMEs. A total of 110 NMEs (86 benign and 24 malignant) from 108 patients were analyzed. Radiological features were recorded. Radiomics features were extracted from manual 3D segmentations using LIFEx software. Multivariate logistic regression (LR) and supervised ML algorithms-LR, support vector machine, random forest, and gradient boosting-were applied. The methodological quality was assessed using the Multicenter Evaluation of Radiomics in Clinical Studies framework. A total of 54 lesions were histopathologically confirmed, and 56 were confirmed by follow-up. Among the 56 lesions evaluated by follow-up, 34 remained stable (≥ 24 months), whereas 22 showed regression (≥ 6 months). Distribution, internal enhancement, size, and laterality differed significantly between benign and malignant NMEs (<i>P</i> < 0.05). Radiomics analysis extracted 123 features, of which 92 on the early and 80 on the late post-contrast images were significant for benign-malignant differentiation (<i>P</i> < 0.05). In the early phase, combining all radiomics features increased specificity from 78% to 98% and accuracy from 82% to 93%. ML models further improved performance, achieving specificity up to 99% and area under the curve (AUC) values exceeding 0.91. Similar improvements were observed on the late phase, with accuracies up to 91% and AUC values up to 0.93. Volumetric 3D radiomics, combined with ML, using early and late post-contrast phases improves diagnostic accuracy and specificity for NME on breast MRI. Integrating 3D radiomics and ML into breast MRI evaluation supports more accurate decision-making in NME and may reduce unnecessary biopsies.