Magnetic resonance imaging findings associated with recurrence in idiopathic granulomatous mastitis.

PURPOSE: To evaluate magnetic resonance imaging (MRI) findings related to recurrence of idiopathic granulomatous mastitis (IGM). METHODS: Demographic data [age, number of births, duration of lactation period, body mass index (BMI) and presence of recurrence] of 71 patients who were diagnosed with IGM were analyzed retrospectively. Characteristics of IGM (maximum width, location, involvement of the retroareolar region, deep tissue, skin), fibroglandular density (FGD), background parenchymal enhancement (BPE), distribution and pattern of contrast enhancement, presence of prepectoral edema, abscesses, fistulae, axillary lymphadenopathies on MRI and apparent diffusion coefficient (ADC) values from the pathological area were recorded. RESULTS: The recurrence rate in patients was 59% (42/71). We found a statistically significant relationship between recurrence and BPE (p = 0.028) and mean ADC (p = 0.035) values (for the cut-off of 1.00 × 10-3 mm2/s; sensitivity = 61.9%, specificity = 69%, AUC = 0.648). However, patients' age (p = 0.346), lactation period (p = 0.470), number of births (p = 0.774), BMI (p = 0.630) maximum width of the area of enhancement (p = 0.112), involvement of the retroareolar region (p = 0.290), deep tissue (p = 0.285), skin (p = 0.230), distribution (p = 0.857) and enhancement pattern (p = 0.157), presence of prepectoral edema (p = 0.094), abscesses (p = 0.441), fistulae (p = 0.809), lymphadenopathies (p = 0.571), and FGT (p = 0.098) were not significantly associated with recurrence. CONCLUSION: Our results revealed that recurrent IGM patients showed high BPE and lower mean ADC values. We think that high BPE and low mean ADC (<1.00 × 10-3 mm2/s) on MRI at the diagnosis stage may be a sign of possible future recurrence, and it will be beneficial to follow the patients more closely and arrange the treatment algorithms accordingly.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models.

RATIONALE AND OBJECTIVES: We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer. METHODS: In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model. RESULTS: Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively. CONCLUSION: ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

RATIONALE AND OBJECTIVES: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in patients with BC, using preoperative MRI images. MATERIALS AND METHODS: This retrospective study included patients with BC with known LVI status and preoperative MRI. The dataset was split into training and unseen testing sets by stratified sampling with a 2:1 ratio. 2D and 3D radiomic features were extracted from contrast-enhanced T1 weighted images (C+T1W) and apparent diffusion coefficient (ADC) maps. The reliability of the features was assessed with two radiologists' segmentation data. Dimension reduction was done with reliability analysis, multi-collinearity analysis, removal of low-variance features, and feature selection. ML models were created with base, tuned, and boosted random forest algorithms. RESULT: A total of 128 lesions (LVI-positive, 76; LVI-negative, 52) were included. The best model performance was achieved with tunning and boosting model based on 3D ADC maps and selected four radiomic features. The area under the curve and accuracy were 0.726 and 63.5% in the training data, 0.732 and 76.7% in the test data, respectively. The overall sensitivity and positive predictive values were 68% and 69.6% in the training data, 84.6% and 78.6% in the test data, respectively. CONCLUSION: ML and radiomics based on 3D segmentation of ADC maps can be used to predict LVI status in BC, with satisfying performance.