Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.

Purpose: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa) in prostate bi-parametric magnetic resonance imaging (MRI) using radiomics features.

Methods: MRI images of patients who were diagnosed with cancer with histopathological confirmation following prostate MRI were collected retrospectively. Patients with a Gleason score of 3+3 were considered to have clinically ciPCa, and patients with a Gleason score of 3+4 and above were considered to have csPCa.

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

Introduction: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images.

Methods: Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions.

Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches.

Purpose: Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV.

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.

Case Report: Role of Interventional Radiology in the Management of Patients with Alveolar Echinococcus: Successful Management of Three Cases.

Alveolar echinococcus (AE) is an infestation by . Partial hepatectomy or liver transplantation is the first choice of treatment. However, the disease is usually diagnosed at an unresectable stage.

Percutaneous Treatment of Non-parasitic Splenic Cysts: Long-Term Results for Single- Versus Multiple-Session Treatment.

Purpose: The aim of this study was to investigate the efficacy and safety of percutaneous sclerotherapy for non-parasitic splenic cysts (NPSCs). The secondary aims were to introduce puncture-aspiration-injection-reaspiration (PAIR) technique in the treatment of NPSCs and to compare multiple- and single-session techniques.

Materials And Methods: This retrospective study included 24 (17 females, 7 males) patients, treated between the years 1997 and 2015.