Unusual inguinal mass: Ectopic seminal vesicle.

A 28-year-old male was referred to our radiology department with the complaint of inguinal mass. He had this mass since its childhood but has recent discomfort. First of all ultrasound was performed and it showed tubular structures connecting with each other. Doppler Ultrasound showed no flow within the tubular mass. At first a thrombosed vascular malformation or lymphocele was considered in differential diagnosis. A scrotal MRI (magnetic resonance imaging) was requested by urology department for further characterization. No right seminal vesicle was seen in its anatomical position and left seminal vesicle is seen in the normal location on MRI (Panel A, axial T2 weighted image; Panel B, coronal T2 weighted image, arrow). There was a tubular cystic mass in right inguinal canal with the same intensity as left seminal vesicle on all sequences (Panel C, Axial T2 weighted image; Panel D, coronal T2 weighted image, arrow). The diagnosis of ectopic seminal vesicle was made. To the best of our knowledge there was no case in literature with an ectopic seminal vesicle. It can be a rare cause of inguinal mass and should be kept in differential diagnosis.

Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO.