Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models.

Rationale And Objectives: To investigate the effectiveness of machine learning-based clinical, radiomics, and combined models in differentiating idiopathic granulomatous mastitis (IGM) from malignancy, both presenting as non-mass enhancement (NME) lesions on magnetic resonance imaging (MRI), and to compare these models with radiological evaluation.

Material And Methods: A total of 178 patients (69 IGM and 109 breast cancer patients) with NME on breast MRI evaluated between March 2018 and April 2022, were included in this two-center study. Age, skin changes, presence of fistula, and abscess were recorded from hospital records.

Prediction of Molecular Subtypes Using Superb Microvascular Imaging and Shear Wave Elastography in Invasive Breast Carcinomas.

Rationale And Objectives: To investigate the efficacy of the advanced imaging methods, superb microvascular imaging (SMI) and shear wave elastography (SWE) in predicting molecular subtypes in invasive breast carcinomas.

Materials And Methods: A total of 210 biopsy-proven breast carcinomas in 200 patients who underwent ultrasound (US) imaging with SMI and SWE were included in this study. Quantitative analyses were performed using mean elasticity (E) score by SWE and vascular index (VI) by SMI.