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Differentiation of Diabetic Macular Edema From Pseudophakic Cystoid Macular Edema by Spectral-Domain Optical Coherence Tomography

Investigative ophthalmology & visual science, 2015-10, Vol.56 (11), p.6724-6733 [Peer Reviewed Journal]

ISSN: 1552-5783 ;EISSN: 1552-5783 ;DOI: 10.1167/iovs.15-17042 ;PMID: 26567783

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  • Title:
    Differentiation of Diabetic Macular Edema From Pseudophakic Cystoid Macular Edema by Spectral-Domain Optical Coherence Tomography
  • Author: Munk, Marion R ; Jampol, Lee M ; Simader, Christian ; Huf, Wolfgang ; Mittermüller, Tamara J ; Jaffe, Glenn J ; Schmidt-Erfurth, Ursula
  • Subjects: Cross-Sectional Studies ; Diabetic Retinopathy - diagnosis ; Diagnosis, Differential ; Humans ; Macular Edema - diagnosis ; Pseudophakia - diagnosis ; Single-Blind Method ; Tomography, Optical Coherence - methods
  • Is Part Of: Investigative ophthalmology & visual science, 2015-10, Vol.56 (11), p.6724-6733
  • Description: To differentiate diabetic macular edema (DME) from pseudophakic cystoid macular edema (PCME) based solely on spectral-domain optical coherence tomography (SD-OCT). This cross-sectional study included 134 participants: 49 with PCME, 60 with DME, and 25 with diabetic retinopathy (DR) and ME after cataract surgery. First, two unmasked experts classified the 25 DR patients after cataract surgery as either DME, PCME, or mixed-pattern based on SD-OCT and color-fundus photography. Then all 134 patients were divided into two datasets and graded by two masked readers according to a standardized reading-protocol. Accuracy of the masked readers to differentiate the diseases based on SD-OCT parameters was tested. Parallel to the masked readers, a computer-based algorithm was established using support vector machine (SVM) classifiers to automatically differentiate disease entities. The masked readers assigned 92.5% SD-OCT images to the correct clinical diagnose. The classifier-accuracy trained and tested on dataset 1 was 95.8%. The classifier-accuracy trained on dataset 1 and tested on dataset 2 to differentiate PCME from DME was 90.2%. The classifier-accuracy trained and tested on dataset 2 to differentiate all three diseases was 85.5%. In particular, higher central-retinal thickness/retinal-volume ratio, absence of an epiretinal-membrane, and solely inner nuclear layer (INL)-cysts indicated PCME, whereas higher outer nuclear layer (ONL)/INL ratio, the absence of subretinal fluid, presence of hard exudates, microaneurysms, and ganglion cell layer and/or retinal nerve fiber layer cysts strongly favored DME in this model. Based on the evaluation of SD-OCT, PCME can be differentiated from DME by masked reader evaluation, and by automated analysis, even in DR patients with ME after cataract surgery. The automated classifier may help to independently differentiate these two disease entities and is made publicly available.
  • Publisher: United States
  • Language: English
  • Identifier: ISSN: 1552-5783
    EISSN: 1552-5783
    DOI: 10.1167/iovs.15-17042
    PMID: 26567783
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    Alma/SFX Local Collection
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