The COVID-19 pandemic had major effects on radiology training programs throughout the country. Many of the challenges were shared, with some variation depending on the size and geographic location of each program. While some initial modifications, such as platoon-type scheduling and redeployment, have been abandoned, other changes such as home workstations and the option of remote conferences have become more permanently incorporated. Remote learning tools and virtual teaching are much more frequently used, although there is emphasis by many programs on preserving in-person training. Programs stressed the importance of communication and adaptability, and getting resident and faculty input is key in optimizing the educational experience.
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http://dx.doi.org/10.1016/j.acra.2023.01.009 | DOI Listing |
Insights Imaging
January 2025
Department of Radiology, Peking University First Hospital, Beijing, 100034, China.
Objectives: To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.
Methods: A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance.
Cell Biol Toxicol
January 2025
Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China.
Thyroid cancer (THCA) is an increasingly common malignant tumor of the endocrine system, with its incidence rising steadily in recent years. For patients who experience recurrence or metastasis, treatment options are relatively limited, and the prognosis is poor. Therefore, exploring new therapeutic strategies has become particularly urgent.
View Article and Find Full Text PDFPediatr Radiol
January 2025
Department of Radiology, College of Medicine, University of Florida, PO Box 100374, Gainesville, FL, 32610-0374, USA.
Purpose: To evaluate whether adult and pediatric trauma center status, as well as the presence of dedicated child protection teams, influences radiology resident performance in detecting non-accidental trauma on the Emergent/Critical Care Imaging Simulation (WIDI SIM) exam.
Materials And Methods: We retrospectively analyzed 639 WIDI SIM exam scores for four pediatric non-accidental trauma cases completed by radiology residents across 33 programs. Residents were stratified by level (R1-R4) and institutional factors, including adult trauma center status, pediatric trauma center status, and child protection team presence.
Ann Hematol
January 2025
Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
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