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Age-Related Scattered Hypofluorescent Spots as an Adverse Prognostic Factor for Polypoidal Choroidal Vasculopathy

Authors
 Seo Hee Kim  ;  Kai Tzu-iunn Ong  ;  Seonghee Choi  ;  Eun Jee Chung  ;  Min Kim  ;  Christopher Seungkyu Lee  ;  Jinyoung Yeo  ;  Eun Young Choi 
Citation
 OPHTHALMOLOGY SCIENCE, Vol.5(5) : 100818, 2025-05 
Journal Title
OPHTHALMOLOGY SCIENCE(Ophthalmology Science)
Issue Date
2025-05
Keywords
Polypoidal choroidal vasculopathy ; Age-related scattered hypofluorescent spots ; Indocyanine green angiography ; Machine learning ; Principal component analysis
Abstract
Purpose : Polypoidal choroidal vasculopathy (PCV) demonstrates significant prognostic variability, and the impact of age-related scattered hypofluorescent spots observed in late-phase indocyanine green angiography (ASHS-LIA) on the prognosis of PCV remains under-researched. This study aims to investigate the association between ASHS-LIA in PCV and prognosis using the AdaBoost machine learning model.
Design : A cross-sectional study.
Participants : The study included patients diagnosed with PCV and treated with anti-VEGF therapy at 2 medical institutions between 2012 and 2021.
Methods : We conducted a retrospective analysis of the clinical characteristics, anti-VEGF treatment history, and outcomes of the participants, classifying them based on the presence or absence of ASHS-LIA. An AdaBoost meta-estimator was applied to predict prognosis, including disease stability, injection frequency, and time to first remission, utilizing features selected through principal component analysis.
Main Outcome Measures : The prognostic significance of ASHS-LIA was assessed by feature importance, with the mean decrease in impurity serving as the evaluation metric.
Results : Of 57 eyes with PCV, 31 exhibited ASHS-LIA and 26 did not. Compared with the non-ASHS-LIA group, the ASHS-LIA group had fewer patients who achieved a super-stable status without recurrence for >18 months postremission (P = 0.03), required a longer time to reach first remission (P = 0.04), and needed more injections (P < 0.001). AdaBoost models confirmed the importance of ASHS-LIA for predicting disease stability, injection demand, and time to first remission, ranking it as the third, seventh, and eighth top contributory factor, respectively.
Conclusions : Machine learning analysis identified ASHS-LIA as a negative prognostic factor in PCV, correlating with reduced disease stability, higher recurrence rates, and increased treatment requirements. These findings suggest that ASHS-LIA could serve as a valuable marker for assessing prognosis and guiding treatment strategies in PCV management.
Financial Disclosure(s) : The author(s) have no proprietary or commercial interest in any materials discussed in this article.
Full Text
https://www.sciencedirect.com/science/article/pii/S2666914525001162
DOI
10.1016/j.xops.2025.100818
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min(김민) ORCID logo https://orcid.org/0000-0003-1873-6959
Lee, Christopher Seungkyu(이승규) ORCID logo https://orcid.org/0000-0001-5054-9470
Choi, Eun Young(최은영) ORCID logo https://orcid.org/0000-0002-1668-6452
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/206223
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