The efficacy of multiparametric prostate magnetic resonance imaging in the diagnosis and treatment of prostate cancer

Aim: To investigate the accuracy of multiparametric prostate magnetic resonance imaging (mpMRI) in determining the diagnosis and treatment options of prostate cancer (PCa), and its pathology correlation. Methods: Between October 2017 and January 2018, 73 patients were subjected to an mpMRI at our clinic. Of these patients, 11 were radical prostatectomy (RP) after treatment, and four were post- radiation therapy (RT) follow-up. The remaining 58 patients were assigned to the PSA elevation and / or positive digital rectal examination (DRE) patient group in this study and their outcomes were evaluated. Results: Of the 58 patients included in the study, 13 were found to have a PI-RADS 5 on mpMRI and in 9 (90%) of 10 patients undergoing simultaneous biopsy, PCa was detected. The biopsy results of all cases evaluated as PI-RADS 1 were benign. All of the patients who were ISUP 3 and above had a PI-RADS 5. Patients with a PI-RADS score of 4 and above being ISUP 2 and above was statistically significant (p=0.011). A case had undergone a previous radical prostatectomy assessment revealed that tPSA increased to 2 ng/ml during the follow-up, and so RT was added to the treatment; although LAP was identified in the left iliac region on an mpMRI performed upon the continued increase of tPSA. During the follow-ups of the patient who had regional RT, the tPSA dropped below 0.01 ng/ml. Conclusion: The results of our study show that mpMRI can gain a new and important place in urology due to the guidance it provides in biopsies, facilitating targeted biopsy, its effectiveness in determining treatment modalities and its importance in post-PCa treatment follow-ups

All-star approach to a small medical imaging dataset: combined deep, transfer, and classical machine learning approaches for the determination of radial head fractures

Background Radial head fractures are often evaluated in emergency departments and can easily be missed. Automated or semi-automated detection methods that help physicians may be valuable regarding the high miss rate. Purpose To evaluate the accuracy of combined deep, transfer, and classical machine learning approaches on a small dataset for determination of radial head fractures. Material and Methods A total of 48 patients with radial head fracture and 56 patients without fracture on elbow radiographs were retrospectively evaluated. The input images were obtained by cropping anteroposterior elbow radiographs around a center-point on the radial head. For fracture determination, an algorithm based on feature extraction using distinct prototypes of pretrained networks (VGG16, ResNet50, InceptionV3, MobileNetV2) representing four different approaches was developed. Reduction of feature space dimensions, feeding the most relevant features, and development of ensemble of classifiers were utilized. Results The algorithm with the best performance consisted of preprocessing the input, computation of global maximum and global mean outputs of four distinct pretrained networks, dimensionality reduction by applying univariate and ensemble feature selectors, and applying Support Vector Machines and Random Forest classifiers to the transformed and reduced dataset. A maximum accuracy of 90% with MobileNetV2 pretrained features was reached for fracture determination with a small sample size. Conclusion Radial head fractures can be determined with a combined approach and limitations of the small sample size can be overcome by utilizing pretrained deep networks with classical machine learning methods

Isolated Tympanic Plate Fracture Frequency and Its Relationship to Mandibular Trauma

Purpose: This study evaluated the prevalence of isolated tympanic fractures and their correlation with mandibular fractures by using maxillofacial computed tomography (CT)

Magnetic Resonance Sialography Findings of Submandibular Ducts Imaging

Purpose. We aimed to assess the problem solving capability of magnetic resonance sialography (MR sialography), a noninvasive method for imaging submandibular gland ducts and determining duct-related pathologies, by comparing diseased and healthy cases. Materials and Methods. We conducted radiological assessment on a total of 60 submandibular glands (mean age 44.7) in 20 cases and 10 volunteers. MR sialography examinations were conducted with single-shot fast spin-echo sequence by using a surface coil placed on the submandibular gland. Each gland was evaluated in terms of the length, width and stricture of the main duct, as well as the difference between the intraparenchymal duct width, and the main duct width. Statistical analysis was performed. Results. In the MR sialography the primary duct mean length was determined as 51 mm (40-57 mm) in all submandibular glands. On the MR sialography imaging, the visualization ratio of the ductal system of submandibular gland was evaluated in the cases and volunteers. Conclusion. MR sialography is an effective and a noninvasive method in imaging submandibular gland ducts, demonstrating the presence, location and degree of stricture/dilatation, and elucidating the disease etiology

THE ROLE OF THE POPLITEAL LYMPH NODES IMAGING FOR DIFFERENTIATING RHEUMATOID ARTHRITIS FROM OSTEOARTHRITIS

WOS: 000265293000005Objective: We wanted to assess the role of the popliteal lymph nodes for differentiating rheumatoid arthritis (RA) from osteoarthritis (OA) by using three dimensional-fast spoiled gradient echo (3D-FSPGR) MR imaging and colour doppler ultrasonography (CDU). Material and Method: MR examination of the knee of 38 casks, 18 with rheumatoid arthritis (RA) and 20 with ostecarthritis (OA) were analyzed retrospectively. MR images were acquired with a 1.5-Tesla magnet. The MR imaging protocol consisted of 3D-FSPGR sequences in three planes. Images were reviewed to describe the number, size and centrally fatty changes of the popliteal lymph nodes. Additionally, these nodes were examined by CDU. Statistical Package for Social Sciences 11.0 program for Windows was performed for statistical analysis. Results: The popliteal nodes were observed in 88.8% of RA cases and 30% of OA cases. The number of the nodes in the RA and OA group was 53, and 8, respectively. The mean size of the lymph nodes in the RA and OA group was 7.4mm, and 3.6mm, respectively. Central fatty was observed in 100% of OA cases and in 60.4% of RA cases. The hiler vascularization was observed in the only 13% of RA cases. Mean value of the RI was determined as 0.62+/-0.005. Conclusion: Differentiating RA from OA, the criteria of the number sizes and central fatty changes of the popliteal lymph nodes showed statistical significance. Performing 3D-FSPGR sequence in knee MR imaging might useful to describe these nodes. The hiler vascularization with low RIs in CDU is also helpful criteria to differentiating RA from OA

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies

Purpose: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. Materials and Methods: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. Results: Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. Conclusion: Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models

Treatment efficacy of ribociclib or palbociclib plus letrozole in hormone receptor-positive/HER2-negative metastatic breast cancer.

Background: Ribociclib, palbociclib and abemaciclib are currently approved CDK4/6 inhibitors along with aromatase inhibitors as the first-line standard-of-care for patients with hormone receptor-positive, HER2-negative metastatic breast cancer. Methods: The authors report retrospective real-life data for 600 patients with estrogen receptor- and/or progesterone receptor-positive and HER2-negative metastatic breast cancer who were treated with ribociclib and palbociclib in combination with letrozole. Results & conclusion: The results demonstrated that the combination of palbociclib or ribociclib with letrozole has similar progression-free survival and overall survival benefit in real life for the patient group with similar clinical features. Specifically, endocrine sensitivity may be a factor to be considered in the treatment preference