Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study

Introduction: Breast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies.Methods: MRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRCYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I protocol, including dynamic contrast-enhanced (DCE) MRCYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method.Results: Following surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence.Conclusion: Texture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis.Implications for practice: The results of this study will enhance knowledge around application and per-formance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.(c) 2023 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.