Literature DB >> 24519427

Detecting glaucoma progression from localized rates of retinal changes in parametric and nonparametric statistical framework with type I error control.

Madhusudhanan Balasubramanian1, Ery Arias-Castro, Felipe A Medeiros, David J Kriegman, Christopher Bowd, Robert N Weinreb, Michael Holst, Pamela A Sample, Linda M Zangwill.   

Abstract

PURPOSE: We evaluated three new pixelwise rates of retinal height changes (PixR) strategies to reduce false-positive errors while detecting glaucomatous progression.
METHODS: Diagnostic accuracy of nonparametric PixR-NP cluster test (CT), PixR-NP single threshold test (STT), and parametric PixR-P STT were compared to statistic image mapping (SIM) using the Heidelberg Retina Tomograph. We included 36 progressing eyes, 210 nonprogressing patient eyes, and 21 longitudinal normal eyes from the University of California, San Diego (UCSD) Diagnostic Innovations in Glaucoma Study. Multiple comparison problem due to simultaneous testing of retinal locations was addressed in PixR-NP CT by controlling family-wise error rate (FWER) and in STT methods by Lehmann-Romano's k-FWER. For STT methods, progression was defined as an observed progression rate (ratio of number of pixels with significant rate of decrease; i.e., red-pixels, to disk size) > 2.5%. Progression criterion for CT and SIM methods was presence of one or more significant (P < 1%) red-pixel clusters within disk.
RESULTS: Specificity in normals: CT = 81% (90%), PixR-NP STT = 90%, PixR-P STT = 90%, SIM = 90%. Sensitivity in progressing eyes: CT = 86% (86%), PixR-NP STT = 75%, PixR-P STT = 81%, SIM = 39%. Specificity in nonprogressing patient eyes: CT = 49% (55%), PixR-NP STT = 56%, PixR-P STT = 50%, SIM = 79%. Progression detected by PixR in nonprogressing patient eyes was associated with early signs of visual field change that did not yet meet our definition of glaucomatous progression.
CONCLUSIONS: The PixR provided higher sensitivity in progressing eyes and similar specificity in normals than SIM, suggesting that PixR strategies can improve our ability to detect glaucomatous progression. Longer follow-up is necessary to determine whether nonprogressing eyes identified as progressing by these methods will develop glaucomatous progression. (ClinicalTrials.gov number, NCT00221897).

Entities:  

Keywords:  Bonferroni correction; Lehmann-Romano; family-wise type I error; glaucoma progression; rate of progression

Mesh:

Year:  2014        PMID: 24519427      PMCID: PMC4586965          DOI: 10.1167/iovs.13-13246

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  25 in total

1.  Technique for detecting serial topographic changes in the optic disc and peripapillary retina using scanning laser tomography.

Authors:  B C Chauhan; J W Blanchard; D C Hamilton; R P LeBlanc
Journal:  Invest Ophthalmol Vis Sci       Date:  2000-03       Impact factor: 4.799

2.  Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain.

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Journal:  Stat Methods Med Res       Date:  2003-10       Impact factor: 3.021

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7.  Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains.

Authors:  E Bullmore; C Long; J Suckling; J Fadili; G Calvert; F Zelaya; T A Carpenter; M Brammer
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8.  Analysis of individual positron emission tomography activation maps by detection of high signal-to-noise-ratio pixel clusters.

Authors:  J B Poline; B M Mazoyer
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9.  Clinical evaluation of the proper orthogonal decomposition framework for detecting glaucomatous changes in human subjects.

Authors:  Madhusudhanan Balasubramanian; Christopher Bowd; Robert N Weinreb; Gianmarco Vizzeri; Luciana M Alencar; Pamela A Sample; Neil O'Leary; Linda M Zangwill
Journal:  Invest Ophthalmol Vis Sci       Date:  2009-08-06       Impact factor: 4.799

10.  Spatiotemporal wavelet resampling for functional neuroimaging data.

Authors:  Michael Breakspear; Michael J Brammer; Ed T Bullmore; Pritha Das; Leanne M Williams
Journal:  Hum Brain Mapp       Date:  2004-09       Impact factor: 5.038

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  1 in total

1.  Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

Authors:  Anna S Mursch-Edlmayr; Wai Siene Ng; Alberto Diniz-Filho; David C Sousa; Louis Arnold; Matthew B Schlenker; Karla Duenas-Angeles; Pearse A Keane; Jonathan G Crowston; Hari Jayaram
Journal:  Transl Vis Sci Technol       Date:  2020-10-15       Impact factor: 3.283

  1 in total

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