Differentiating Malignant From Benign Breast Masses: A Multiparametric Magnetic Resonance Imaging (Mri)-Based Radiomics Approach With Machine Learning Publisher Pubmed



Arian A ; Azhdeh S ; Ahmadinejad N ; Taherzadeh M ; Shoja Shafiee F ; Akbari Zare H ; Delazar S ; Alipour S ; Omranipour R ; Amanpourgharaei B
Source: Clinical Radiology Published:2026

Abstract

AIM We evaluated the performance of radiomics models based on multiparametric magnetic resonance imaging (MRI; dynamic contrast-enhanced, T2, and apparent diffusion coefficient (ADC) sequences) in differentiating malignant from benign masses. Various combinations of MRI sequences were compared. MATERIALS AND METHODS One hundred forty-four patients with breast lesions (69 malignant and 75 benign) were retrospectively analysed. After manual segmentation, 107 shape- and texture-based features were extracted from each lesion. Support vector classifier, random forest, and k-nearest neighbours were trained to distinguish malignant and benign lesions. The models were evaluated with seven different input image sets: five single-sequence images and two combined sets (all sequences vs all contrast-free sequences). RESULTS Single-sequence models achieved similar accuracies for ADC, pre-contrast T1, and post-contrast T1, while T2 models showed slightly lower accuracy. The all-sequence model achieved an average accuracy of 0.88, outperforming the single-sequence models. The contrast-free sequence models (T2, ADC, and pre-contrast T1) yielded an average accuracy of 0.90, comparable to the all-sequence model. CONCLUSION Non-contrast MRI radiomics algorithms have the potential for clinical implementation as complementary tools, potentially eliminating the need for contrast in MRI studies. © 2025 The Royal College of Radiologists.