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. Two experienced radiologists evaluated MRI images according to the breast imaging reporting and data system 2013 lexicon. Lesions were segmented independently on T2-weighted, apparent diffusion coefficient, and post-contrast-T1-weighted sequences. Data were split into training and external testing sets. Machine learning models were built using Light GBM (light gradient-boosting machine). Radiological, clinical, radiomics, and clinical-radiomics models were created and compared. Decision curve analysis was performed. Quality of reporting and that of methodology were evaluated using CLEAR and METRICS tools. RESULTS: IGM group was younger (p = 0.014). Abscesses (p < 0.001), fistulas (p < 0.001), and skin changes (p < 0.001) were significantly more common in the IGM group. No significant difference was detected in terms of lesion size (p = 0.213). In the evaluation of NME, the lowest performance belonged to the radiologists' evaluation (AUC for training, 0.740; for testing, 0.737), while the highest AUC was achieved by the model developed by combined clinical and radiomics features (AUC for training, 0.979; for testing, 0.942). CONCLUSION: Our study has shown that the machine learning-based clinical-radiomics model might have the potential to accurately discriminate IGM and malignant lesions in evaluating NME areas.

Ewing sarcoma of the mandible: A rare case report and literature review.

Ewing sarcoma (ES) usually arises from long bones and affects the head and neck region in only 1%-4% of cases. We reported clinical, radiographic, cytomorphologic, and histomorphologic findings of the ES in the mandible, because of its rarity and radiologically misinterpreted as a parotid gland tumor. A 26-year-old male patient presented with a history of painfull cheek swelling. On magnetic resonance imaging, a mass measuring 50 × 48 × 45 mm was found eroding mandible and pushing back the parotid gland. Aspiration cytology was performed with suspicion of parotid gland tumor. Small, nucleated cells with nuclear indentation, inconspicuous nucleoli, and occasionally rosette-like arrangement were observed. Neuroendocrine immune markers were positive on cell block. It was diagnosed as small round cell neoplasm with neuroendocrine differentiation and biopsy was suggested. The differential diagnosis considered soft tissue and parotid gland tumors. The small round cell tumor morphology was seen on biopsy specimen and immunostaining was applied. The diagnosis for this case was ES of the mandible. ES of the mandible is unusual. Although the histogenesis is still unknown, various cells have been proposed as cells of origin namely, endothelial, hematopoietic, fibroblastic, mesenchymal stem cells or neural derived mesenchymal stem cells. Small cell morphology, CD99, CD56, neuron specific enolase, and synaptophysin expressions confirmed the diagnosis of ES. The differentiation of the ES from other small cell tumors may be difficult and requires awareness for histological and immunohistochemical features. It should be kept in mind that the diagnosis can be challenging due to uncommon locations and radiological misinterpreted.

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.

Comparison of the efficiency of systemic therapy and intralesional steroid administration in the treatment of idiopathic granulomatous Mastitis. The novel treatment for Granulomatous Mastitis.

BACKGROUND: Idiopathic granulomatous mastitis that has not had a clear consensus about its treatment since the day it was identified as a rare, benign inflammatory breast disease that mimics malignancy due to its appearance features. AIMS: In our research, we intended to compare the efficiency of intralesional and systemic steroids administration in the treatment of idiopathic granulomatous mastitis. STUDY DESIGN: Prospective randomized controlled study. METHODS: A total of 36 female patients who had been histopathologically diagnosed with idiopathic granulomatous mastitis and whose other factors had been microbiologically excluded were included in the study. The patients were randomized into two sub-groups that would be treated with systemic and intralesional steroids. All patients were evaluated through physical examination one week after the completion of the treatment. Subsequently, the follow-up of the patients was performed thorough physical examination and ultrasonography and/or magnetic resonance imaging at the 1st, 3rd, and 6th months. RESULTS: All patients adapted to treatment. Complete clinical regression occurred in 32 patients, while 30 of 36 patients responded to treatment both radiologically and clinically. A total of 4 patients had minor side effects. It was determined that there was no statistically significant difference between local and systemic steroid groups in terms of complete clinical regression, responded to treatment side effects, and recurrence rates. CONCLUSION: Intralesional steroid administration was also considered just as a successful treatment method as the systemic steroid administration. KEY WORDS: Idiopathic granulomatous mastitis, Intralesional steroid, Systemic steroid.

Characterization of Suspicious Microcalcifications on Mammography Using 2D Shear-Wave Elastography.

Our aim was to investigate the correlations between the findings of two-dimensional shear-wave elastography (2D-SWE) and the histopathologic results of microcalcifications (MCs) visualized using ultrasonography (USG). Fifty people with suspicious MCs without accompanying mass were evaluated. They underwent USG and 2D-SWE before USG-guided tru-cut biopsy. SWE values and histopathologic features were compared statistically. The variables between groups were analyzed using the Mann-Whitney U test. Receiver operating characteristic analysis was performed and cut-off values determined to discriminate malignancy, invasiveness and high grade. Pathology confirmed 27 malignant lesions (18 invasive ductal carcinomas, one invasive lobular and eight ductal carcinomas in situ) and 23 benign ones. There was a statistically significant difference between the SWE values of malignant and benign MCs (p < 0.001). The diagnostic performance of SWE for malignancy, invasiveness and high grade were as follows, repectively: sensitivity (93%, 83%, 88%), specificity (91%, 88%, 53%), positive predictive value (93%, 94%, 44%), negative predictive value (91%, 70%, 90%) and area under the curve (0.952, 0.885, 0.776). Cut-off values were determined as 57 kPa for malignancy, 124 kPa for invasiveness and 124.5 kPa for high grade. In conclusion, SWE is a useful method in clinical practice for characterizing MCs that can be visualized with USG.

Localized Breast Amyloidosis.

Localized amyloidosis in the breast is a very rare disease and may mimic malignant lesions. A 60-year-old woman who had a history of breast-conserving surgery presents with a new a well-defined oval opacity accompanied by many round tight clustered micro- and macrocalcifications on mammograms. It could not be visualized sonographically due to the intense posterior acoustic shadowing of the fat necrosis areas and contrast enhancement was not detected in this area on the dynamic contrast enhanced magnetic resonance images. At pathological examination breast amyloidosis was detected. Amyloidosis of the breast is a rare disease, but it can mimic malignancy and should be included in the differential diagnosis.

Diffusion-Weighted Imaging for the Discrimination of Benign and Malignant Breast Masses; Utility of ADC and Relative ADC.

PURPOSE: To determine the contribution of apparent diffusion coefficient (ADC), and relative ADC (rADC) values to differentiate between benign and malignant breast masses. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) of the breast with diffusion-weighted imaging (DWI) of patients with benign or malignant breast masses diagnosed either by histopathological findings or by follow-up imaging were evaluated retrospectively. Histopathological analyses were performed for 71 lesions (80.7%) while the remaining were followed up every six months for one year. DWI was performed using b-values of 0 and 1000 sec/mm2, and ADC and rADC were calculated and compared. A receiver operating characteristic (ROC) curve and Youden index were used to evaluate the parameter's optimal threshold and diagnostic value. Statistical significance was set as p < 0.05. RESULTS: Eighty-eight lesions from a total of 81 patients, aged between 16 and 73 (mean age 42 ± 11.3) years were obtained and evaluated. Pathological results of 34 (38.6%) out of 71 lesions were malignant and 37 lesions (42%) were benign. Seventeen (19.3%) lesions remained stable at one-year follow-up and were accepted as benign breast masses. Mean ADC values of benign and malignant lesions were 1.584 × 10-3mm2/sec and 0.884 × 10-3mm2/sec (p < 0.05), respectively. Sensitivity and specificity of ADC were 88% and 87%, respectively at a cut-off value of 1.04 × 10-3mm2/sec. Mean rADC was 0.931 for benign lesions and 0.557 for malignant lesions (p < 0.05). Sensitivity and specificity were 82% and 83% at a cut-off value of 0.639. No prominent superiority of rADC over ADC is identified in the differentiation of breast masses. CONCLUSION: ADC and rADC values derived from DWI can be equally useful in clinical setting to differentiate benign from malignant breast masses.

The correlation between biological activity and diffusion-weighted MR imaging and ADC value in cases with prostate cancer.

PURPOSE: Firstly, we aimed to investigate the correlation among dynamic contrasted magnetic resonance (MR) images, diffusion-weighted MR images, and apparent diffusion coefficent (ADC) values in patients with prostate cancer. Secondly, we aimed to investigate the roles of these variables on clinical risk classification and the biological behavior of the prostate cancer. METHODS: A total of sixty with prostatic adenocarcinoma patients diagnosed between January 2011 and May 2013 were retrospectively included in the study. Risk classification of patients were evaluated as low-risk (Group 1) (n = 20) (Stage T1c-T2a, PSA < 10 ng/ml, Gleason Score < 7), moderate-risk (Group 2) (n = 18) (Stage T1b-T2c, PSA = 10-20 ng/ml, Gleason Score = 7) and high-risk (Group 3) (n = 22) (Stage > T3a, PSA > 20 ng/ml, Gleason Score > 7). Diffusion-weighted MR images, dynamic contrasted MR images, and ADC values of the prostates were correlated. RESULTS: ADC values of the cases in Group 3 were lower than those of the other groups (p < 0.001). ADC values of the areas without malignancy did not differ significantly between groups (p > 0.05). Biological activity of the tumor tissue was determined by GS, while a negative correlation was observed between GSs and ADC values of the patients, (p < 0.001). CONCLUSION: In tumors with higher Gleason scores, lower ADC values were obtained. These measured values can play a role in the noninvasive determination of the cellularity of the tumoral mass.

Effectiveness of additional diagnostic parameters in magnetic resonance mammography: a comparative study with the BI-RADS classification and scoring system.

OBJECTIVE: This study aims to evaluate the strength of magnetic resonance (MR) lesion descriptors for malignancy and to determine the effectiveness of a scoring system that combines BI-RADS parameters with additional criteria. MATERIALS AND METHODS: Three hundred thirty-nine histopathologically proven lesions that had undergone MR imaging were analyzed retrospectively. Based on the Fischer scoring system, an optimal cutoff value was calculated for combined parameters. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for all lesions with MR BI-RADS classification without using additional parameters. Finally, the results of the scoring system and MR BI-RADS classification were compared. RESULTS: The optimal cutoff value according to the total score was calculated as 5. The sensitivity and the specificity of BI-RADS classification were calculated to be 94.20% and 56.12%, respectively. The scoring system using a cutoff value of 5 resulted in a little loss of sensitivity (89.86%) but resulted in a reasonable increase in the specificity (88.49%). CONCLUSIONS: Additional parameters can improve the specificity of MR imaging. T2-weighted signal features, adjacent vessel sign, unilateral-focal edema, and hook sign were considered as effective parameters.