Luis de Sisternes1, Noah Simon2, Robert Tibshirani3, Theodore Leng4, Daniel L Rubin5. 1. Department of Radiology, Stanford University, Stanford, California, United States. 2. Department of Statistics, Stanford University, Stanford, California, United States Department of Biostatistics, University of Washington, Seattle, Washington, United States. 3. Department of Statistics, Stanford University, Stanford, California, United States. 4. Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California, United States. 5. Department of Radiology, Stanford University, Stanford, California, United States Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, United States.
Abstract
PURPOSE: We developed a statistical model based on quantitative characteristics of drusen to estimate the likelihood of conversion from early and intermediate age-related macular degeneration (AMD) to its advanced exudative form (AMD progression) in the short term (less than 5 years), a crucial task to enable early intervention and improve outcomes. METHODS: Image features of drusen quantifying their number, morphology, and reflectivity properties, as well as the longitudinal evolution in these characteristics, were automatically extracted from 2146 spectral-domain optical coherence tomography (SD-OCT) scans of 330 AMD eyes in 244 patients collected over a period of 5 years, with 36 eyes showing progression during clinical follow-up. We developed and evaluated a statistical model to predict the likelihood of progression at predetermined times using clinical and image features as predictors. RESULTS: Area, volume, height, and reflectivity of drusen were informative features distinguishing between progressing and nonprogressing cases. Discerning progression at follow-up (mean, 6.16 months) resulted in a mean area under the receiver operating characteristic curve (AUC) of 0.74 (95% confidence interval [CI], 0.58, 0.85). The maximum predictive performance was observed at 11 months after a patient's first early AMD diagnosis, with mean AUC 0.92 (95% CI, 0.83, 0.98). Those eyes predicted to progress showed a much higher progression rate than those predicted not to progress at any given time from the initial visit. CONCLUSIONS: Our results demonstrate the potential ability of our model to identify those AMD patients at risk of progressing to exudative AMD from an early or intermediate stage. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
PURPOSE: We developed a statistical model based on quantitative characteristics of drusen to estimate the likelihood of conversion from early and intermediate age-related macular degeneration (AMD) to its advanced exudative form (AMD progression) in the short term (less than 5 years), a crucial task to enable early intervention and improve outcomes. METHODS: Image features of drusen quantifying their number, morphology, and reflectivity properties, as well as the longitudinal evolution in these characteristics, were automatically extracted from 2146 spectral-domain optical coherence tomography (SD-OCT) scans of 330 AMD eyes in 244 patients collected over a period of 5 years, with 36 eyes showing progression during clinical follow-up. We developed and evaluated a statistical model to predict the likelihood of progression at predetermined times using clinical and image features as predictors. RESULTS: Area, volume, height, and reflectivity of drusen were informative features distinguishing between progressing and nonprogressing cases. Discerning progression at follow-up (mean, 6.16 months) resulted in a mean area under the receiver operating characteristic curve (AUC) of 0.74 (95% confidence interval [CI], 0.58, 0.85). The maximum predictive performance was observed at 11 months after a patient's first early AMD diagnosis, with mean AUC 0.92 (95% CI, 0.83, 0.98). Those eyes predicted to progress showed a much higher progression rate than those predicted not to progress at any given time from the initial visit. CONCLUSIONS: Our results demonstrate the potential ability of our model to identify those AMDpatients at risk of progressing to exudative AMD from an early or intermediate stage. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
Authors: Yuhua Zhang; Xiaolin Wang; Pooja Godara; Tianjiao Zhang; Mark E Clark; C Douglas Witherspoon; Richard F Spaide; Cynthia Owsley; Christine A Curcio Journal: Retina Date: 2018-01 Impact factor: 4.256
Authors: Luis de Sisternes; Gowtham Jonna; Jason Moss; Michael F Marmor; Theodore Leng; Daniel L Rubin Journal: Biomed Opt Express Date: 2017-02-28 Impact factor: 3.732
Authors: Rebecca J Sardell; Muneeswar G Nittala; Larry D Adams; Reneé A Laux; Jessica N Cooke Bailey; Denise Fuzzell; Sarada Fuzzell; Lori Reinhart-Mercer; Laura J Caywood; Violet Horst; Tine Mackay; Debbie Dana; SriniVas R Sadda; William K Scott; Dwight Stambolian; Jonathan L Haines; Margaret A Pericak-Vance Journal: Ophthalmology Date: 2016-10-19 Impact factor: 12.079