Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

Journal: Scientific reports
PMID: 40251316

Abstract

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic features extracted from pre-treatment optical coherence tomography (OCT) images. Retrospective data from 234 eyes with DME treated with three anti-VEGF therapies between January 2020 and March 2024 were collected from two clinical centers. Radiomic analysis was conducted on pre-treatment OCT images. Following principal component analysis (PCA) for dimensionality reduction, two unsupervised clustering methods (K-means and hierarchical clustering) were applied. Baseline characteristics and treatment outcomes were compared across clusters to assess clustering efficacy. Feature selection employed a three-stage pipeline: exclusion of collinear features (Pearson's r > 0.8); sequential filtering through ANOVA (P < 0.05) and Boruta algorithm (500 iterations); multivariate stepwise regression (entry criteria: univariate P < 0.1) to identify outcome-associated predictors. From 1165 extracted radiomic features, four distinct DME clusters were identified. Cluster 4 exhibited a significantly lower incidence of residual/recurrent DME (RDME) (34.29%) compared to Clusters 1-3 (P = 0.003, P = 0.005 and P = 0.002, respectively). This cluster also demonstrated the highest proportion of eyes (71.43%) with best-corrected visual acuity (BCVA) exceeding 20/63 (P = 0.003, P = 0.005 and P = 0.002, respectively). Multivariate analysis identified logarithm_gldm_DependenceVariance as an independent risk factor for RDME (OR 1.75, 95% CI 1.28-2.40; P < 0.001), while Wavelet-LH_Firstorder_Mean correlated with worse visual outcomes (OR 8.76, 95% CI 1.22-62.84; P = 0.031). Unsupervised ML leveraging pre-treatment OCT radiomics successfully stratifies DME eyes into clinically distinct subgroups with divergent therapeutic responses. These quantitative features may serve as non-invasive biomarkers for personalized outcome prediction and retinal pathology assessment.

Authors

  • Xuemei Liang
    Department of Ophthalmology, Aier Eye Hospital, Jinan University, No, 191, Huanshi Middle Road, Yuexiu District, Guangzhou, 510071, Guangdong, People's Republic of China.
  • Shaozhao Luo
    Department of Ophthalmology, Aier Eye Hospital, Jinan University, No, 191, Huanshi Middle Road, Yuexiu District, Guangzhou, 510071, Guangdong, People's Republic of China.
  • Zhigao Liu
    Department of Ophthalmology, Jinan Aier Eye Hospital, No. 1916, Erhuan East Road, Licheng District, Jinan City, Shandong Province, People's Republic of China.
  • Yunsheng Liu
    Department of Ophthalmology, Cenxi Aier Eye Hospital, No. 101, Yuwu Avenue, Cenxi City, Wuzhou City, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Shinan Luo
    Department of Ophthalmology, Nanning Aier Eye Hospital, No, 63, Chaoyang Road, Xingning District, Nanning, 530012, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Kaiqing Zhang
    Department of Ophthalmology, Aier Eye Hospital, Jinan University, No, 191, Huanshi Middle Road, Yuexiu District, Guangzhou, 510071, Guangdong, People's Republic of China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

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