- Non-Hemorrhagic Subependymal Echogenic Lesions in Neonates: Ultrasound–MRI Correlation and Clinical Significance
IRB Number: IRB202600006
Brief Summary: Isolated subependymal echogenic lesions extending to the caudothalamic groove are often considered highly suggestive of germinal matrix hemorrhage in premature infants. However, not all echogenic lesions demonstrate corresponding hemorrhagic signal characteristics on MRI. This retrospective study aims to identify imaging features predictive of non-hemorrhagic subependymal echogenic lesions, incorporating available follow-up imaging to differentiate these findings from germinal matrix hemorrhage. The study also explores the developmental and anatomical background underlying these imaging appearances.
Principal Investigator: Ensar Yekeler, MD
Study Coordinators: Ibrahim Tuna, MD, Michael Weiss, MD
- A Developmental Finding of Torcular Pseudomass: MRI Characteristics and Incidence from the Prenatal Period to Adulthood
IRB Number: IRB202600007
Brief Summary: Incidental MRI findings adjacent to the confluence of sinuses may mimic a mass-like lesion yet lack imaging characteristics suggestive of true pathology. This study aims to characterize the MRI features and incidence of torcular pseudomass across the lifespan, from the prenatal period through adulthood, to improve diagnostic confidence and avoid unnecessary clinical concern or intervention.
Principal Investigator: Ensar Yekeler, MD
Study Coordinator: Ibrahim Tuna, MD
- Focal Nodular Hyperplasia Following Childhood Malignancies: A “Do Not Touch” Lesion with Characteristic Imaging Findings
IRB Number: IRB202501656
Brief Summary: Focal nodular hyperplasia (FNH) is an uncommon tumor-like hepatic lesion in children with an unclear etiology. It may also develop following treatment for pediatric malignancies, where it can mimic tumor recurrence or a secondary malignancy. Recognition of its characteristic imaging features is essential to ensure accurate diagnosis and to avoid unnecessary biopsy or intervention.
Principal Investigator: Ensar Yekeler, MD
Study Coordinator: —
- Enhancing Detection of Actionable Lung Nodules on Chest X-Rays Using Artificial Intelligence: An Observational Study
IRB Number: IRB202500388
Brief Summary: This retrospective observational study will evaluate chest X-ray examinations in patients aged 35 years and older to assess whether an artificial intelligence–based computer-aided detection tool (qXR-LN) improves identification of high-risk lung nodules compared with standard radiology reports.
Principal Investigator: Bruno Hochhegger, MD, PhD
Study Coordinator: —
- Simulation Performance Evaluation of Radiology Residents Completing the WIDI Plain Film Modules
IRB Number: IRB202400415
Brief Summary: The University of Florida Department of Radiology has developed plain radiography education modules as part of the Wisdom in Diagnostic Imaging (WIDI) platform. This study aims to evaluate the effectiveness of these modules in teaching plain radiography interpretation to radiology residents and PGY-1 emergency medicine residents at the University of Florida using simulation-based performance assessment.
Principal Investigator: Anthony Mancuso, MD
Study Coordinator: —
- Image Reconstruction Optimization for I-123 MIBG Studies
IRB Number: IRB202500545
Brief Summary: I-123 metaiodobenzylguanidine (MIBG) is a radiopharmaceutical used in SPECT/CT imaging to identify neuroendocrine tumors. Clinical concerns regarding image quality in I-123 MIBG studies prompted an investigation to optimize the current image reconstruction protocol using phantom imaging. Image quality was assessed across more than 500 alternative reconstruction protocols. In this study, the top-performing protocols will be applied to patient images and evaluated by a nuclear medicine radiologist.
Principal Investigator: Stephanie Leon, PhD
Study Coordinator: —
- An AI-Based MRI Approach to Distinguishing Pseudoprogression from True Progression in Postoperative Glioblastoma Patients
IRB Number: IRB202500176
Brief Summary: This study applies artificial intelligence algorithms to the analysis of postoperative MRI scans in patients with glioblastoma to differentiate pseudoprogression from true tumor progression based solely on imaging features. By relying exclusively on MRI data, the study aims to streamline diagnostic assessment and support more accurate and timely clinical decision-making.
Principal Investigator: Ibrahim Tuna, MD
Study Coordinators:
Md Mahfuz Al Hasan, PhD
Kyle B. See, PhD
- Comparison of the Accuracy of a Large Language Model Versus Human Evaluators in Grading Diagnostic Radiology Resident Interpretations
IRB Number: IRB202500509
Brief Summary: This study aims to assess the accuracy of a large language model in grading written interpretations of anonymized, de-identified imaging studies from an existing educational database. Model-generated grades will be compared with evaluations provided by human reviewers to determine concordance and potential educational utility.
Principal Investigator: Cooper Dean, MD
Study Coordinator: —
- Retrospective Analysis of the Accuracy of Two Peak Skin Dose Tracking Software Systems Used in an Interventional Radiology Suite
IRB Number: IRB202401660
Brief Summary: This retrospective study will use a previously validated peak skin dose (PSD) calculation code to evaluate the accuracy of two dose tracking systems used in an interventional radiology suite at UF Health: a commercial dose tracking platform and a vendor-specific software solution. Radiation dose structured reports from patients who have undergone interventional procedures will be analyzed using the PSD code and compared with the outputs of both tracking systems to assess their accuracy.
Principal Investigator: Megan Glassell, PhD
Study Coordinator: —
- BillionToOne & UF GI Oncology Group Circulating Biomarker Project
IRB Number: IRB202202837
Brief Summary: Determine concordance between BillionToOne’s treatment response assay with standard-of-care response determination
Principal Investigator: Thomas George
Study Coordinator: Kayleigh Ratliff, Benjamin Burgess, Laura Coppola, Brittany Lansford