The Diagnostic Role of Shear Wave Elastography and Superb Microvascular Imaging in the Evaluation of Suspicious Microcalcifications.

OBJECTIVES: The aim of this study was to investigate the role of superb microvascular imaging (SMI) and shear wave elastography (SWE) in the prediction of malignancy and invasiveness of isolated microcalcifications (MC) that can be visualized by ultrasonography (US). MATERIAL AND METHODS: Sixty-seven women with MC, who were considered suspicious on mammography were evaluated. Only those lesions that could be visualized by US and presented as non-mass lesion were included. They were evaluated by B-mode US, SMI, and SWE before US-guided core-needle biopsy. B-mode US, SMI (vascular index (SMIvi)), and SWE (E-mean, E-ratio) findings were compared with histopathologic features. RESULTS: Pathology confirmed 45 malignant (21 invasive and 24 in situ carcinomas) and 22 benign lesions. There was a statistically significant difference between malignant and benign groups in terms of size (P = .015), distortion (P = .028), cystic component (P < .001), E-mean (P < .001), E-ratio (P < .001), and SMIvi (P = .006). For differentiation of invasiveness E-mean (P = .002), E-ratio (P = .002), and SMIvi (P = .030) were statistically significant. According to ROC analysis E-mean (cut-off point at 38 kPa) was the most sensitive (78%) and the most specific (95%) value among four numeric parameters (size, SMI, E-mean, and E-ratio) with AUC = 0.895, PPV = 97%, and NPV = 68% in detecting malignancy. In the evaluation of invasiveness, the most sensitive (71.4%) method was SMI (cut-off point at 3.4) and the most specific (72%) method was E-mean (cut-off point at 91.5 kPa). CONCLUSION: Our study shows that adding SWE and SMI to the sonographic evaluation of MC would be an advantage for US-guided biopsy. Including suspicious areas according to SMI and SWE in the sampling area can help target the invasive part of the lesion and avoid underestimation of core biopsy.

Detachment and embolization of totally implantable central venous access devices: diagnosis and management.

BACKGROUND: Detachment and embolization (DE) is a rare complication of totally implantable central venous access devices (TIVADs). This study aimed to analyze clinical findings, etiology, and treatment options in DE of TIVADs. METHODS: Patients who experienced DE between 2010-2019 were included. Indications, implantation techniques, time to diagnosis, patient complaints, diagnostic methods, rupture site, location of embolization, treatment methods, and chest X-rays prior to detachment were analyzed retrospectively. RESULTS: DE of TIVAD was detected in 12(1.2%) patients. Eleven patients had breast cancer and one had colon cancer. Mean age at implantation was 45.3 ± 9.6(31-61.3) years. Seven (58%) patients were asymptomatic, four (33.3%) had TIVAD malfunction, and one (8.3%) had pain and swelling at port site after injection. Mean time from implantation to diagnosis was 1149.92(16-2795) days. The etiologies comprised Pinch-off Syndrome (POS) in eight (66%) patients, detachment directly adjacent to the lock mechanism in three (25%) patients, and probable iatrogenic injury during explantation in one (9%) patient. The most common site of embolism was the superior vena cava (25%). While the embolized fragment was removed percutaneously in 11 patients, medical follow-up was treatment choice for one patient. CONCLUSIONS: DE is a rare complication with an incidence rate of 1.2% in this study. Since most patients were asymptomatic, chest radiography plays an important role in diagnosis. The most common cause was POS, and it can be prevented by inserting the catheter from lateral third of the clavicle during subclavian vein catheterization. The first-choice treatment was percutaneous femoral retrieval. However, if not technically possible, alternative treatment options are thoracotomy or follow-up with anticoagulant therapy.

Role of Combining Grayscale Findings With Superb Microvascular Imaging and Shear Wave Elastography in Standardization and Management of NON-MASS Breast Lesions.

ABSTRACT: The non-mass breast lesions on ultrasound (US) are a group of challenging pathology. We aimed to standardize these grayscale findings and investigate the effectiveness of superb microvascular imaging (SMI) and shear wave elastography (SWE). A total of 195 lesions were evaluated by B-mode US, SWE, and SMI in the same session. A "NON-MASS model" was built on grayscale US to group the lesions only as areas and those with associated features: microcalcifications, architectural distortion, ductal changes, and microcysts. The mean stiffness parameters Emean, Eratio, and mean vascular index (VI) were recorded following consecutive measurements. Besides, the microvascularity was graded based on Adler's classification (grades 0 to 3). Lesions were divided into 3 groups: benign, category B3, and malignant. One hundred twelve (57.4%) lesions were benign, 23 (11.8%) were B3, and 60 were (30.8%) in the malignant category. Thirty-eight (19.5%) lesions were observed only as an area, whereas associated features were present in 157 lesions (80.5%). Distortion was the only associated feature predicting malignancy among the grayscale findings (P < 0.001). There was a significant difference between malignant and nonmalignant (benign and B3) groups in terms of Adler's grade, Emean, Eratio, and VI values (P < 0.001). Sensitivity, specificity, and accuracy increased when advanced imaging parameters were added to grayscale findings (P < 0.001). In the presence of microcalcifications, architectural distortion, high elasticity, and hypervascularity in the "NON-MASS" imaging model, the suspicion of malignancy increases. The non-mass findings and advanced imaging techniques have the potential to find greater coverage in the following versions of BI-RADS atlas.

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.

Artificial Intelligence in BI-RADS Categorization of Breast Lesions on Ultrasound: Can We Omit Excessive Follow-ups and Biopsies?

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. MATERIALS AND METHODS: A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient's clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment. RESULTS: This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups. CONCLUSION: AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices.

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.

Three Cases of Breast Metastases from Lung Cancer and Systematic Review of the Literature.

Despite the high prevalence of lung cancer among other primary tumors, metastasis of this particular malignancy in the breast is very rare. We report three new cases of lung cancer with breast metastases and discuss radiological and clinical findings. Radiologically, each case displayed different characteristics. First, one of them had bilateral superficially and deeply located irregular lesions. Second, the patient presented with findings similar to inflammatory breast cancer. The third case had a circumscribed mass, resembling a benign complicated cyst. To guide clinicians for proper patient management, radiologists should be aware of the scope of typical and atypical imaging findings of metastatic involvement of the breast.