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Imaging and Molecular Biomarkers: The New Approach to Degenerative Retinal Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Ophthalmology".

Deadline for manuscript submissions: 28 December 2025 | Viewed by 846

Special Issue Editors


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Guest Editor
IRCCS—Fondazione Bietti, Roma, Italy
Interests: diabetic retinopathy; radiation retinopathy; retinal imaging; OCT; retinal glial cells; ocular oncology; proteomics; liquid biopsy; precision medicine

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Guest Editor
Department of Ophthalmology, University of Padova, Padova, Italy
Interests: diabetic retinopathy; age-related macular degeneration; retinal dystrophy; retinal imaging; microperimetry; OCT; OCT angiography; von Hippel-Lindau disease; geographic atrophy; retinal glial cells
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Special Issue Information

Dear Colleagues,

Degenerative retinal diseases, such as diabetic retinopathy and age-related macular degeneration, represent major causes of irreversible vision loss globally. Despite significant advancements, early diagnosis, prognostic assessment, and the effective monitoring of treatment responses continue to present significant clinical challenges. This Special Issue will highlight the latest advancements in imaging modalities—such as optical coherence tomography (OCT) and OCT angiography—and molecular biomarkers that can deepen our understanding of disease mechanisms and support precision medicine approaches.

This Special Issue will showcase original research and review articles that explore novel imaging techniques, biomarker discovery, multimodal diagnostic strategies, and translational studies linking molecular data with clinical phenotypes. Contributions addressing artificial intelligence applications, longitudinal monitoring, and the integration of imaging and omics data are particularly encouraged.

Through the inclusion of interdisciplinary expertise spanning ophthalmology, molecular biology, and data science, this Special Issue will promote innovation and collaborative advancements in this field. We encourage contributions from researchers and clinicians that advance knowledge and address current diagnostic and therapeutic challenges in retinal degenerative diseases.

Dr. Giulia Midena
Dr. Elisabetta Pilotto
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • ophthalmology
  • degenerative retinal diseases
  • imaging biomarkers
  • multimodal imaging
  • OCT
  • OCT angiography
  • molecular biomarkers
  • precision medicine
  • translational ophthalmic research

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Published Papers (1 paper)

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Research

12 pages, 259 KB  
Article
AI-Assisted OCT Clinical Phenotypes of Diabetic Macular Edema: A Large Cohort Clustering Study
by Edoardo Midena, Marco Lupidi, Lisa Toto, Giuseppe Covello, Daniele Veritti, Elisabetta Pilotto, Maria Vittoria Cicinelli, Rosangela Lattanzio, Michele Figus, Giulia Midena, Luca Danieli, Enrico Borrelli, Michele Reibaldi, Daniele Tognetto, Leandro Inferrera, Simone Donati, Settimio Rossi, Paolo Melillo, Paolo Lanzetta, Valentina Sarao, Giulia Gregori, Carlo Cagini, Chiara Maria Eandi, Adriano Carnevali, Vincenzo Scorcia, Emilia Maggio, Grazia Pertile, Ciro Costagliola, Gilda Cennamo, Paolo Mora, Roberto Dell’Omo, Marzia Affatato, Marzia Passamonti, Mariacristina Parravano, Nicola Vito Lassandro, Marco Nassisi, Francesco Viola, Niccolò Castellino, Francesco Cappellani, Giuseppe Giannaccare, Francesco Boscia, Maria Oliva Grassi, Donatella Musetti, Valentina Folegani, Alessandro Invernizzi, Luca Rossetti, Tommaso Bacci, Federico Ricci, Marco Lombardo, Mary Romano, Nicola Valsecchi, Michele Coppola, Fabiano Cavarzeran and Luisa Frizzieroadd Show full author list remove Hide full author list
J. Clin. Med. 2025, 14(22), 7893; https://doi.org/10.3390/jcm14227893 - 7 Nov 2025
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Abstract
Purpose: To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. Methods: A cross-sectional study was conducted on OCT scans collected from 2355 [...] Read more.
Purpose: To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. Methods: A cross-sectional study was conducted on OCT scans collected from 2355 eyes of 1688 patients with DME and performed during real-world clinical practice. OCT scans were automatically analyzed by a software able to automatically quantify OCT key biomarkers: intraretinal fluid (IRF), subretinal fluid (SRF), hyperreflective retinal foci (I-HRF), and external limiting membrane (ELM) and ellipsoid zone (EZ) interruption. Clustering analysis was performed using the above-mentioned biomarkers, including the distribution of IRF across the three ETDRS rings. Results: The overall population was predominantly composed of type 2 diabetes patients (89%), with a mean diabetes duration of 15.6 ± 10.7 years and mean best corrected visual acuity (BCVA) of 63 ± 18 ETDRS letters. Multivariate clustering identified four morphological phenotypes with distinct patterns of fluid distribution associated with different I-HRF counts, SRF volume, and percentages of ELM/EZ integrity (p < 0.0001). Conclusions: This large OCT analysis identified distinct morphological subtypes of DME, confirming the clinical relevance of key imaging biomarkers. The distribution and severity of DME features differ among clusters, supporting the importance of OCT-based phenotyping in tailoring treatment strategies and understanding disease evolution. Full article
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