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Purpose: Predicting the progression of intermediate AMD (iAMD) to neovascular AMD (nAMD) will help to identify high-risk patients and improve treatment outcomes. The present study assessed whether choroidal OCT biomarkers could predict conversion to nAMD.
Methods: This retrospective study included patients with clinically stable iAMD who either converted to nAMD (C group) or did not convert (NC group) during one year of follow-up. OCT parameters included subfoveal choroidal thickness (SFCT), central macular thickness (CMT), Haller vascular thickness (HVT), inner choroidal thickness (ICT), and double-layer sign (DLS).
Results: Of 116 total eyes, there were 37 in the NC group and 79 in the C group. Baseline SFCT was significantly lower in the C group compared to the NC group (169.0 ± 63.2 μm vs. 218.0 ± 97.8 μm, p = 0.01). Baseline HVT and ICT were lower in the C group (105.2 ± 40.6 μm vs. 121.0 ± 56.6 μm, p = 0.17 and 61.9 ± 35.5 μm vs. 77.5 ± 41.7 μm, p = 0.09). HVT was decreased at all time points in the C group vs NC (p > 0.05). The ICT was reduced in the C group at each time point except at conversion time (p > 0.05). Of all eight eyes who presented DLS at baseline, 100% converted to nAMD (p < 0.001).
Conclusion: Lower SFCT at baseline may signal conversion to nAMD within 12 months.
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http://dx.doi.org/10.1007/s00417-024-06611-w | DOI Listing |
J Appl Clin Med Phys
September 2025
Clinical Imaging Physics Group, Duke University Health System, Durham, North Carolina, USA.
Introduction: Medical physicists play a critical role in ensuring image quality and patient safety, but their routine evaluations are limited in scope and frequency compared to the breadth of clinical imaging practices. An electronic radiologist feedback system can augment medical physics oversight for quality improvement. This work presents a novel quality feedback system integrated into the Epic electronic medical record (EMR) at a university hospital system, designed to facilitate feedback from radiologists to medical physicists and technologist leaders.
View Article and Find Full Text PDFJ Intensive Care
September 2025
German Center for Vertigo and Balance Disorders, Ludwig-Maximilians-Universitat (LMU), University Hospital Grosshadern, Munich, Germany.
Background: Survivors of critical illness frequently face physical, cognitive and psychological impairments after intensive care. Sensorimotor impairments potentially have a negative impact on participation. However, comprehensive understanding of sensorimotor recovery and participation in survivors of critical illness is limited.
View Article and Find Full Text PDFDiagn Pathol
September 2025
Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Background: Gastric cancer is one of the most common cancers worldwide, with its prognosis influenced by factors such as tumor clinical stage, histological type, and the patient's overall health. Recent studies highlight the critical role of lymphatic endothelial cells (LECs) in the tumor microenvironment. Perturbations in LEC function in gastric cancer, marked by aberrant activation or damage, disrupt lymphatic fluid dynamics and impede immune cell infiltration, thereby modulating tumor progression and patient prognosis.
View Article and Find Full Text PDFBMC Psychol
September 2025
Behavioral Epidemiology, Institute of Clinical Psychology and Psychotherapy, TUD Dresden University of Technology, Chemnitzer Straße 46, 01187, Dresden, Germany.
Background: Disruptive behavior and emotional problems - especially anxiety - are common in children and frequently co-occur. However, the role of co-occurring emotional problems in disruptive behavior intervention response is unclear. This study aimed to compare the effectiveness of an indicated prevention program in children with disruptive behavior problems with vs.
View Article and Find Full Text PDFBMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.