Evidence map›Paper›PMID 41794975›Full record

ArticleScientific reports2026

Integrating genetics, age and imaging to predict treatment outcomes in neovascular age-related macular degeneration: a proof-of-concept study.

Ismail Moghul, Nikolas Pontikos, Anitta Sharma, Timing Liu, Taha Soomro, Siegfried K Wagner, Alan Sousa Silva, Gongyu Zhang, Gunjan Naik, Pallavi Bagga and 6 more

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Ismail MoghulUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Nikolas PontikosUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Anitta SharmaUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Timing LiuUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Taha SoomroMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Siegfried K WagnerMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Alan Sousa SilvaNIHR Biomedical Research Centre at Moorfields Eye Hospital &, UCL Institute of Ophthalmology, London, UK.
Gongyu ZhangKing's College London, London, UK.
Gunjan NaikNIHR Biomedical Research Centre at Moorfields Eye Hospital &, UCL Institute of Ophthalmology, London, UK.
Pallavi BaggaMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Yiu Wai ChanMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Pearse A KeaneUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Valentina CiprianiQueen Mary University of London, London, UK.
Sobha SivaprasadUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Andrew R WebsterUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK.
Konstantinos BalaskasUCL Institute of Ophthalmology, 11-43 Bath Street, London, EC1V 9EL, UK. konstantinos.balaskas@gmail.com.

Funding

Moorfields Eye Charity R190001
6 · The paper itself

Abstract

To evaluate the feasibility of integrating genetic, imaging, and demographic data for predictive modelling of treatment outcomes in neovascular age-related macular degeneration (nAMD). Proof-of-concept retrospective cohort study with prospective DNA collection. Patients with unilateral nAMD receiving anti-vascular endothelial growth factor (anti-VEGF) therapy on a treat-and-extend regimen at a single tertiary centre were recruited. Polygenic risk scores (PRS) for AMD were derived from genotyping data (NIHR Bioresource). Optical coherence tomography (OCT) biomarkers-intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM)-were automatically quantified using a deep learning segmentation model. Predictors of treatment outcomes included PRS, age at first injection, and OCT feature volumes at baseline. XGBoost was used for binary outcomes and linear regression for continuous outcomes, employing five-fold cross-validation. (1) macular dryness (no IRF/SRF) at 24 months, (2) average treatment interval in year 2, and (3) age at first injection. 106 participants were included. The multimodal model integrating age, imaging, and PRS predicted macular dryness at 24 months with AUC = 0.903, outperforming imaging alone (AUC = 0.701). PRS was associated with younger age at first injection (β = –4.69, 95% CI [–8.93, –0.44], P = 0.031) but not with treatment burden (β = –6.39, P = 0.13). Integrating PRS with OCT-derived imaging biomarkers and patient age is technically feasible and improves predictive performance of modelling for anatomical treatment outcomes in nAMD. PRS reflects genetic susceptibility to nAMD and contextualizes the predictive value of imaging biomarkers for treatment response.

Indexed as

Macular DegenerationAgedAged, 80 and overAngiogenesis InhibitorsFemaleGenetic Risk ScoreHumansMalePredictive Learning ModelsProof of Concept StudyRetrospective StudiesTomography, Optical CoherenceTreatment OutcomeVascular Endothelial Growth Factor AAngiogenesis InhibitorsVascular Endothelial Growth Factor AAge-related macular degenerationArtificial intelligenceGeneticsImagingMultimodal AIPredictive modelling

Identifiers

PMID41794975
PMCPMC13086832

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.