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Macular Telangiectasia (MacTel) is a rare retinal vascular disorder, with Type 3a MacTel being a distinct form characterized by retinal ischemia with the classical findings of MacTel, such as juxtafoveal telangiectasis, right-angled venules, and deep capillary plexus involvement without central nervous system findings. This case presents a novel X-shaped lesion pattern and ischemic features, expanding the known imaging spectrum of MacTel. A 53-year-old male with diabetes and a history of aripiprazole use presented with persistent blurred vision, a black curtain sensation, and metamorphopsia in the right eye. Visual acuity was 0.8 in the right eye and 1.0 in the left. A multimodal imaging approach, including fundus photography, fundus autofluorescence (FAF), fluorescein angiography (FFA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), was used to evaluate structural and vascular abnormalities. Fundus examination revealed an X-shaped hypopigmented lesion with central pigmentation. FAF showed hypoautofluorescence, indicating chronic RPE loss, and no loss of foveal autofluorescence was observed. FFA demonstrated progressive hyperfluorescence with perifoveal aneurysmal and telangiectatic vessels, along with a slightly enlarged foveal avascular zone (FAZ), suggesting ischemic involvement. OCT revealed intraretinal cysts, a disruption of the ellipsoid zone and external limiting membrane, pigment epithelial detachment, and increased choroidal backscattering. OCTA confirmed right-angled venules, aneurysmal telangiectatic vessels, and localized ischemia predominantly affecting the deep capillary plexus. This case highlights a rare variant of Type 3a MacTel with a unique X-shaped lesion. The presence of juxtafoveal telangiectasis, vascular occlusion, right-angled venules, and deep capillary plexus changes supports the diagnosis. Multimodal imaging played a critical role in characterizing the disease and differentiating it from other macular disorders, contributing to an expanded understanding of the clinical and imaging spectrum of MacTel.
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http://dx.doi.org/10.3390/diagnostics15060754 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
Cereb Cortex
August 2025
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Ophthalmology, The First Affiliated Hospital of Dalian Medical University.
Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, with the affected population projected to reach 270 million by 2045. Our study analyzed 2 434 interventional trials registered between 2007 and 2024 in the Informa Pharma Intelligence database and found that anti-VEGF agents dominate the therapeutic landscape-bevacizumab represents 24.0 % of studies, ranibizumab 15.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
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