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