Breast hematoma with active bleeding due to seat belt injury.

Breast emergencies are not frequent but play an important part in routine breast imaging applications. Diagnosis and identification of seat belt injury in emergency department are essential for patient management and early treatment of advanced cases. Herein we reported imaging findings of a patient who had prominent swollen at her left breast accompanying tissue edema and painful palpable mass formed by active bleeding hematoma as a result of seat belt injury due to a car accident. Radiologic examinations revealed hematoma in the breast accompanying active bleeding.

Use of shear-wave elastography to distinguish complex and complicated fibroadenomas from simple fibroadenomas.

PURPOSE: Simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), and cellular fibroadenomas (CeFAs) are variants of fibroadenomas. Additionally, some degenerative, hyperplastic, and metaplastic changes may occur in fibroadenomas, forming complicated fibroadenomas. Distinctive ultrasonography (US) features in variants of fibroadenomas and complicated fibroadenomas have not been reported. Shear-wave elastography (SWE) can be applied to effectively discriminate between these variants and complicated fibroadenomas. In this study, we aimed to evaluate SWE findings to discriminate between SFAs and other variants. METHODS: In total, 48 patients (26 with SFAs, 16 with CFAs, 3 with CeFAs, and 3 with complicated fibroadenomas) participated in this study. The lesions were classified into two groups according to histopathologic diagnoses. The SWE evaluation and lesion elasticity scores (Emax, Emean, and Emin) were both assessed in m/s and k/Pa, respectively. Two observers measured Emax, Emean, and Emin. Brightness (B)-mode US findings based on the Breast Imaging Reporting and Data System categorization and elasticity scores were recorded. In the statistical analyses, the chi-square test and non-parametric tests were performed. Fisher's exact test was used to compare independent groups, and Spearman's correlation coefficients were used to correlate the SWE data between the two observers. Additionally, receiver operating characteristic curves were analyzed to evaluate the diagnostic performance of the elasticity values. RESULTS: The B-mode US features in both groups showed no statistical significance. The set of SWE values of both observers demonstrated strong statistical significance in discriminating between group 1 (SFAs) and Group 2 (CFAs, CeFAs, and complicated fibroadenomas). CONCLUSION: As the fibroadenoma variants and complicated fibroadenomas have similar US findings, SWE in addition to a conventional B-mode examination can increase the diagnostic performance to discriminate SFAs from other complex and complicated forms of fibroadenomas.

Outcomes of high-risk breast lesions diagnosed using image-guided core needle biopsy: results from a multicenter retrospective study.

PURPOSE: The clinical management of high-risk lesions using image-guided biopsy is challenging. This study aimed to evaluate the rates at which such lesions were upgraded to malignancy and identify possible predictive factors for upgrading high-risk lesions. METHODS: This retrospective multicenter analysis included 1.343 patients diagnosed with high-risk lesions using an image-guided core needle or vacuum-assisted biopsy (VAB). Only patients managed using an excisional biopsy or with at least one year of documented radiological follow-up were included. For each, the Breast Imaging Reporting and Data System (BI-RADS) category, number of samples, needle thickness, and lesion size were correlated with malignancy upgrade rates in different histologic subtypes. Pearson's chi-squared test, the Fisher-Freeman-Halton test, and Fisher's exact test were used for the statistical analyses. RESULTS: The overall upgrade rate was 20.6%, with the highest rates in the subtypes of intraductal papilloma (IP) with atypia (44.7%; 55/123), followed by atypical ductal hyperplasia (ADH) (38.4%; 144/375), lobular neoplasia (LN) (12.7%; 7/55), papilloma without atypia (9.4%; 58/611), flat epithelial atypia (FEA) (8.7%; 10/114), and radial scars (RSs) (4.6%; 3/65). There was a significant relationship between the upgrade rate and BI-RADS category, number of samples, and lesion size Lesion size was the most predictive factor for an upgrade in all subtypes. CONCLUSION: ADH and atypical IP showed considerable upgrade rates to malignancy, requiring surgical excision. The LN, IP without atypia, pure FEA, and RS subtypes showed lower malignancy rates when the BI-RADS category was lower and in smaller lesions that had been adequately sampled using VAB. After being discussed in a multidisciplinary meeting, these cases could be managed with follow-up instead of excision.

Machine learning analysis of adrenal lesions: preliminary study evaluating texture analysis in the differentiation of adrenal lesions

PURPOSE: This study aimed to determine the accuracy of texture analysis in differentiating adrenal lesions on unenhanced computed tomography (CT) images. METHODS: In this single-center retrospective study, 166 adrenal lesions in 140 patients (64 women, 76 men; mean age 56.58 ± 13.65 years) were evaluated between January 2015 and December 2019. The lesions consisted of 54 lipid-rich adrenal adenomas, 37 lipid-poor adrenal adenomas (LPAs), 56 adrenal metastases (ADM), and 19 adrenal pheochromocytomas (APhs). Each adrenal lesion was segmented by manually contouring the borders of the lesion on unenhanced CT images. A texture analysis of the CT images was performed using Local Image Feature Extraction software. First-order and second-order texture parameters were assessed, and 45 features were extracted from each lesion. One-Way analysis of variance with Bonferroni correction and the Mann-Whitney U test was performed to determine the relationships between the texture features and adrenal lesions. Receiver operating characteristic curves were performed for lesion discrimination based on the texture features. Logistic regression analysis was used to generate logistic models, including only the texture parameters with a high-class separation capacity (i.e., P < 0.050). SPSS software was used for all statistical analyses. RESULTS: First-order and second-order texture parameters were identified as significant factors capable of differentiating among the four lesion types (P < 0.050). The logistic models were evaluated to ascertain the relationships between LPA and ADM, LPA and APh, and ADM and APh. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the first model (LPA vs. ADM) were 85.7%, 70.3%, 81.3%, 76.4%, and 79.5%, respectively. The sensitivity, specificity, PPV, NPV, and accuracy of the second model (LPA vs. APh) were all 100%. The sensitivity, specificity, PPV, NPV, and accuracy of the third model (ADM vs. APh) were 87.5%, 82%, 36.8%, 98.2%, and 82.7%, respectively. CONCLUSION: Texture features may help in the characterization of adrenal lesions on unenhanced CT images.

Evaluation of abdominal computed tomography findings in patients with COVID-19: a multicenter study

PURPOSE: To evaluate the frequency of abdominal computed tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19) and interrogate the relationship between abdominal CT findings and patient demographic features, clinical findings, and laboratory test results as well as the CT atherosclerosis score in the abdominal aorta. METHODS: This study was designed as a multicenter retrospective study. The abdominal CT findings of 1.181 patients with positive abdominal symptoms from 26 tertiary medical centers with a positive polymerase chain-reaction test for severe acute respiratory syndrome coronavirus 2 were reviewed. The frequency of ischemic and non-ischemic CT findings as well as the association between CT findings, clinical features, and abdominal aortic calcific atherosclerosis score (AA-CAS) were recorded. RESULTS: Ischemic and non-ischemic abdominal CT findings were detected in 240 (20.3%) and 328 (27.7%) patients, respectively. In 147 patients (12.4%), intra-abdominal malignancy was present. The most frequent ischemic abdominal CT findings were bowel wall thickening (n = 120; 10.2%) and perivascular infiltration (n = 40; 3.4%). As for non-ischemic findings, colitis (n = 91; 7.7%) and small bowel inflammation (n = 73; 6.2%) constituted the most frequent disease processes. The duration of hospital stay was found to be higher in patients with abdominal CT findings than in patients without any positive findings (13.8 ± 13 vs. 10.4 ± 12.8 days, P < 0.001). The frequency of abdominal CT findings was significantly higher in patients who did not survive the infection than in patients who were discharged after recovery (41.7% vs. 27.4%, P < 0.001). Increased AA-CAS was found to be associated with a higher risk of ischemic conditions in abdominal CT examinations. CONCLUSION: Abdominal symptoms in patients with COVID-19 are usually associated with positive CT findings. The presence of ischemic findings on CT correlates with poor COVID-19 outcomes. A high AA-CAS is associated with abdominal ischemic findings in patients with COVID-19.

Multimodality imaging and histopathology of metaplastic breast cancer

Metaplastic breast cancer (MBC) is a rare subtype of invasive breast cancer characterized by mixed epithelial and mesenchymal differentiation. Commonly seen subtypes include squamous cell carcinoma, spindle cell carcinoma, and metaplastic carcinoma with heterologous mesenchymal differentiation. MBC tends to have a more aggressive clinical presentation, higher metastatic potential, higher rates of local recurrence, and a worse prognosis compared with invasive breast carcinoma of no special type. Most MBCs are triple-negative breast cancers, which explains their resistance to most systemic therapies. Therefore, accurately diagnosing MBC early is crucial for deciding the treatment strategy and predicting the prognosis. In this pictorial essay, the imaging findings of MBC in different modalities and the histopathologic features of its subtypes are reviewed.

Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors.

PURPOSE: Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. METHODS: The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. RESULTS: In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. CONCLUSION: In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.

Accuracy of specific free air distributions in predicting the localization of gastrointestinal perforations.

PURPOSE: To evaluate the role of specific distributions of free air in predicting the location of gastrointestinal (GI) tract perforation. MATERIALS AND METHODS: One hundred and fifteen patients with surgically proven GI tract perforation between January 2015 and June 2019 were included in the study. The site of perforation was based on surgical findings in all cases. Two radiologists retrospectively interpreted the computed tomography (CT) images of these patients for extraluminal free air distribution. Perforation sites were demonstrated intraoperatively in all cases and were categorized as follows: stomach and duodenum, jejunum and ileum, proximal colon (cecum, ascending colon, and transverse colon), distal colon (descending colon and sigmoid colon), rectum, and appendix. RESULTS: There were 79 male and 36 female patients with a mean age of 56.4 years. Periportal, perihepatic, and perigastric free air were statistically significant in predicting gastroduodenal perforation. Mesenteric free air was significant in predicting both small bowel and distal colon perforations. Pelvic free air was statistically significant in distal colon perforations. Periappendiceal free air was found to be a strong predictor of acute perforated appendicitis. CONCLUSION: Specific free air distributions may help to predict the site of gastrointestinal perforation, which would change the treatment plan.

[Evaluation of solid pulmonary nodules in patients with bladder cancer with computed tomography based texture analysis method: Is it possible to differentiate metastatic - non-metastatic nodules?] / Bilgisayarli tomografi tabanli doku analizi yöntemi ile mesane kanserli hastalarda solid pulmoner nodüllerin degerlendirilmesi: Metastatik - metastatik olmayan nodül ayrimini yapmak mümkün mü?

INTRODUCTION: Solid Pulmonary Nodule (SPN) is defined as parenchymal radiopacity smaller than 3 cm in diameter. Evaluating the metastatic nature of the SPNs detected in the thorax computed tomography (TCT) examination for staging purposes in cancer patients becomes a fundamental problem for the physician. Invasive procedures, additional imaging or follow-up imaging, are often used to differentiate metastatic and non-metastatic nodules. In this study, we aimed to distinguish SPNs detected in patients diagnosed with bladder cancer (BC) as metastatic and non-metastatic nodules by texture analysis. MATERIALS AND METHODS: TCT images of patients diagnosed with BC in our hospital from January 2007 until December 2017 were retrospectively evaluated. A total of 46 patients with SPN, including metastatic (n= 19) and non-metastatic (n= 27), were included in the study. Short axis diameter, long-axis diameter, nodule volume and volume histogram values of the nodules were obtained. Chisquare test was used to evaluate dependent variables, and the Mann-Whitney U test was used to evaluate independent variables. ROC curves of the obtained data were plotted. Statistically, the significant p-value was determined as less than 0.05. RESULT: A significant difference was found between SPN long axis, short axis and volume values. In the volumetric histogram analysis, the maximum density value and the mean density value were found to be statistically significant. When the average of the highest densities in the volume histogram data was evaluated, the area under the curve value was 0.702 (95% CI, 519-854). The metastatic nodule could be distinguished with a sensitivity of 88% and a specificity of 70% when the volume histogram has the maximum density threshold of 50 HU. CONCLUSIONS: In this study, we concluded that SPN detected on CT images can be distinguished as metastatic and non-metastatic nodules using texture analysis method without invasive procedures.

Retroperitoneal extensive free air bubbles due to decompression illness.

Decompression illness (DCI) is a rare condition caused by air bubbles that arise because of a rapid decrease in ambient pressure. These air bubbles exert both physical and chemical effects associated with a range of findings from asymptomatic clinical presentation to death. In the literature, changes in consciousness, severe musculoskeletal and abdominal pain, respiratory distress, and skin changes have been described. The diagnosis of DCI is difficult, but anamnesis and physical examination are helpful. Radiologic evaluation is useful for determining possible complications in patients with severe disease and excluding other acute pathologies. In computed tomography (CT) images of patients diagnosed with DCI, air bubbles in the portal venous system, iliac and mesenteric veins, the vena cava inferior (VCI), and the cerebral and spinal arteries have been described before. Herein, we present the clinical and CT findings of two cases of DCI with extensive intra-abdominal free bubbles evident on abdominal CT.

Possible radiologic renal signs of COVID-19.

Coronavirus disease 2019 (COVID-19) has caused a huge outbreak worldwide. Patients infected with COVID-19 most commonly present with respiratory tract symptoms and pneumonia. Gastrointestinal symptoms can also occur in these patients. Renal involvement presenting as acute renal infarct and/or acute kidney injury has been described in literature; however, these typically occur in patients with severe COVID-19. To the best of our knowledge, there are no reported cases describing abnormal renal imaging findings in a patient with only mild form of COVID-19. We, therefore, report a case of a patient with mild COVID-19 presenting with acute abdominal pain and acute renal infarct.

Shear-Wave Elastography-Guided Core Needle Biopsy for the Determination of Breast Cancer Molecular Subtype.

OBJECTIVE: To investigate whether shear-wave elastography (SWE) guidance during core-needle biopsy can improve diagnostic accuracy and accurate determination of the molecular subtypes of breast cancer. METHODS: This controlled, randomized prospective cohort study included 58 patients (mean age: 56.9 ± 16.2) who were referred for image-guided core-needle biopsy between May 2018 and April 2019 for lesions larger than 1 cm. In Group 1, 30 lesions were biopsied without SWE guidance and recorded as Biopsy A. In Group 2, 30 lesions were examined with SWE before biopsy, and then two different parts of the lesions were biopsied; biopsies from the relatively rigid areas of the lesions were recorded as Biopsy B, and biopsies from the less rigid areas of the lesions were recorded as Biopsy C. The histopathological and immunohistochemical results of biopsies were compared with the surgical results. RESULTS: The sensitivity of Biopsy A, B and C were 96.7%, 100% and 100%, respectively. The benign-malignant concordance rates were 94.7%, 100%, and 90% and the diagnostic concordance rates were 89.5%, 100%, and 90% in Biopsies A, B, and C, respectively. When the 10% differences in the estrogen receptor (ER), progesterone receptor (PR), and Ki67 rates were considered significant, the concordance rate of ER was highest in Biopsy B (77.8%; p = 0.040). The concordance rate of immunohistochemical subtyping was 100% in Biopsy B and 71.4% in Biopsies A and C (p = 0.086). CONCLUSION: SWE-guided core-needle biopsy of breast lesions increased the sensitivity, diagnostic accuracy, and accuracy of immunohistochemical subtyping to 100%.

Fibroadenomas: a multidisciplinary review of the variants.

Fibroadenomas are the most common benign breast lesions in women. Hormonal activities, particularly an increase in oestrogenic activity, have an important role in the aetiology of fibroadenomas. They are commonly detected incidentally. Most fibroadenomas have similar imaging findings and these lesions can be defined as simple fibroadenomas (SFAs). However, due to different histopathologic characteristics and components, variants of fibroadenomas have been described. These variants include; juvenile, giant, complex, myxoid, cellular, and hyalinised fibroadenomas. The diagnoses of these variants are important. They have different clinical behaviours, potential for malignant transformation, and treatment strategies. We present imaging findings of SFAs and other variants of fibroadenomas. To our knowledge, there is no review in which imaging findings of all fibroadenoma variants are presented together.

Evaluation of Elastographic Features in Complex Fibroadenomas With Radiologic-Pathologic Correlation.

Fibroadenoma is the most common benign tumor of the breast, and complex fibroadenoma (CFA) is one of its variants. Of the fibroadenomas, 22% are CFAs, and in women with CFAs, the malignancy development is found to be higher than in women with noncomplex fibroadenomas. Although there is an increased risk of malignancy with CFAs, the imaging findings of CFAs are fundamentally similar to those of other variants of fibroadenomas. In the literature, B-mode ultrasound features of CFAs were reported in detail. To our knowledge, there is no study that has specifically described the elastographic findings of CFAs. This article aims to illustrate the elastographic features of CFAs and to correlate radiologic and histopathologic findings of different cases.

Conventional Imaging and Sonoelastography Findings of Oncocytic Breast Carcinoma in a Man.

Oncocytic breast carcinoma (OBC) is one of the rare types of invasive breast carcinoma in according to the classification of The World Health Organization. Herein we represent imaging findings of a case of 69-year-old male patient with OBC.

Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors.

OBJECTIVES: American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions can be distinguished from BI-RADS 3 lesions with main ultrasound (US) findings such as a well-defined contour, round/oval shape, and parallel orientation with a homogeneous echo pattern. Breast Imaging Reporting and Data System 4A solid masses might be diagnosed as simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), or benign phyllodes tumors (BPTs). Complex fibroadenomas have an increased risk of invasive cancer development than SFAs, and BPTs have a risk of borderline-malignant phyllodes tumor transformation; both of them are surgically treated, whereas follow-up procedures are applied in SFAs. It is essential to differentiate SFAs from CFAs and BPTs. Grayscale features of these lesions include a prominent overlap. Texture analyses in breast lesions have contributions in benign-malignant lesion differentiation. In this study, we aimed to use texture analysis of US images to differentiate these benign lesions. METHODS: Grayscale US features of lesions (32 SFAs, 31 CFAs, and 32 BPTs) were classified according to the BI-RADS. Texture analysis of US images with LIFEx software (http://www.lifexsoft.org) was performed retrospectively. First- and second-order histogram parameters were evaluated. RESULTS: In grayscale US, the shape, orientation, and posterior acoustic characteristics had statistical significance (P < .05). In the statistical analysis, skewness, kurtosis, excess kurtosis, gray-level co-occurrence matrix (GLCM)-energy, GLCM-entropy log 2, and GLCM-entropy log 10 revealed significant differences among all 3 groups (P < .05). CONCLUSIONS: As grayscale US features show prominent intersections, and treatment options differ, correct diagnosis is essential in SFAs, CFAs, and BPTs. In this study, we concluded that texture analysis of US images can discriminate SFAs from CFAs and BPTs. Texture analyses of US images is a potential candidate diagnostic tool for these lesions, and accurate diagnoses will preclude patients from undergoing unnecessary biopsies.