title: Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, United States.
authors.forename: Luis
authors.initials: L
authors.lastname: de Sisternes
authors.affiliation: Department of Statistics, Stanford University, Stanford, California, United States Department of Biostatistics, University of Washington, Seattle, Washington, United States.
authors.forename: Noah
authors.initials: N
authors.lastname: Simon
authors.affiliation: Department of Statistics, Stanford University, Stanford, California, United States.
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.affiliation: Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California, United States.
authors.forename: Theodore
authors.initials: T
authors.lastname: Leng
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, United States Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, United States.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008266:Macula Lutea / Q000473:pathology*; D008268:Macular Degeneration / Q000175:diagnosis*; D008297:Male; D012372:ROC Curve; D015203:Reproducibility of Results; D055213:Retinal Pigment Epithelium / Q000473:pathology*; D012189:Retrospective Studies; D013997:Time Factors; D041623:Tomography, Optical Coherence / Q000379:methods*
keywords: age-related macular degeneration; optical coherence tomography; prediction; risk assessment; statistical modeling