Prediction of testicular atrophy based on sonographic findings immediately after successful manual detorsion.

Purpose: To investigate and determine the sonographic findings obtained from manually distorted testes to predict testicular atrophy following manual detorsion.

Materials And Methods: Twenty-two patients who had been diagnosed with testicular torsion and undergone manual detorsion were included. These patients were classified according to the presence or absence of testicular atrophy.

Pulmonary arterial pseudoaneurysm as an unusual complication of pulmonary actinomycosis: Two case reports.

We report 2 cases of pulmonary actinomycosis complicated by a pseudoaneurysm. In Case 1, a 67-year-old man visited a hospital 7 months ago because of hemoptysis. CT revealed a suspected lung abscess in the left lingular segment; however, no diagnosis was confirmed by bronchoscopy.

Differentiation of carcinosarcoma from endometrial carcinoma on magnetic resonance imaging using deep learning.

Purpose: To verify whether deep learning can be used to differentiate between carcinosarcomas (CSs) and endometrial carcinomas (ECs) using several magnetic resonance imaging (MRI) sequences.

Material And Methods: This retrospective study included 52 patients with CS and 279 patients with EC. A deep-learning model that uses convolutional neural networks (CNN) was trained with 572 T2-weighted images (T2WI) from 42 patients, 488 apparent diffusion coefficient of water maps from 33 patients, and 539 fat-saturated contrast-enhanced T1-weighted images from 40 patients with CS, as well as 1612 images from 223 patients with EC for each sequence.

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists.

Purpose: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions.

Methods: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs.

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments.

Background: This study aimed to compare deep learning with radiologists' assessments for diagnosing ovarian carcinoma using MRI.

Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis.

Preoperative Diagnosis of a Torsioned Accessory Spleen.

We report a case of a 12-year-old boy with an accessory spleen torsion. He presented with left-sided abdominal pain after trauma. A 4 cm oval mass without contrast enhancement was detected on contrast-enhanced computed tomography (CT), and ultrasound (US) showed a 4 cm oval mass below the spleen.

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists.

Purpose: To compare deep learning with radiologists when diagnosing uterine cervical cancer on a single T2-weighted image.

Methods: This study included 418 patients (age range, 21-91 years; mean, 50.2 years) who underwent magnetic resonance imaging (MRI) between June 2013 and May 2020.