Characterization of Errors in Retinopathy of Prematurity Diagnosis by Ophthalmologists-in-Training in the United States and Canada.

PURPOSE: To identify the prominent factors that lead to misdiagnosis of retinopathy of prematurity (ROP) by ophthalmologists-in-training in the United States and Canada. METHODS: This prospective cohort study included 32 ophthalmologists-in-training at six ophthalmology training programs in the United States and Canada. Twenty web-based cases of ROP using wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Responses were compared to a consensus reference standard diagnosis for accuracy, which was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. The types of diagnostic errors that occurred were analyzed with descriptive and chi-squared analysis. Main outcome measures were frequency of types (category, zone, stage, plus disease) of diagnostic errors; association of errors in zone, stage, and plus disease diagnosis with incorrectly identified category; and performance of ophthalmologists-in-training across postgraduate years. RESULTS: Category of ROP was misdiagnosed at a rate of 48%. Errors in classification of plus disease were most commonly associated with misdiagnosis of treatment-requiring (plus error rate = 16% when treatment-requiring was correctly diagnosed vs 81% when underdiagnosed as type 2 or pre-plus; mean difference: 64.3; 95% CI: 51.9 to 76.7; P < .001) and type 2 or pre-plus (plus error rate = 35% when type 2 or pre-plus was correctly diagnosed vs 76% when overdiagnosed as treatment-requiring; mean difference: 41.0; 95% CI: 28.4 to 53.5; P < .001) disease. The diagnostic error rate of postgraduate year (PGY)-2 trainees was significantly higher than PGY-3 trainees (PGY-2 category error rate = 61% vs PGY-3 = 35%; mean difference, 25.4; 95% CI: 17.7 to 33.0; P < .001). CONCLUSIONS: Ophthalmologists-in-training in the United States and Canada misdiagnosed ROP nearly half of the time, with incorrect identification of plus disease as a leading cause. Integration of structured learning for ROP in residency education may improve diagnostic competency. [J Pediatr Ophthalmol Strabismus. 2023;60(5):337-343.].

Association between myopia progression and quantity of laser treatment for retinopathy of prematurity.

BACKGROUND: Previous studies found that infants with retinopathy of prematurity (ROP) who were treated for more posterior disease with a greater number of laser spots developed higher myopia. These studies included multiple physicians with variations in laser density. In treatments by a single physician, laser spot count is a better surrogate for area of avascular retina and anterior-posterior location of disease, so that the relationship with myopia can be better assessed. METHODS: Our retrospective study included infants treated with laser for ROP by a single surgeon at a single center. Exclusion criteria were irregularities during laser and additional treatment for ROP. We assessed correlation between laser spot count and change in refractive error over time using a linear mixed effects model. RESULTS: We studied 153 eyes from 78 subjects treated with laser for ROP. The average gestational age at birth was 25.3±1.8 weeks, birth weight 737±248 grams, laser spot count 1793±728, and post-treatment follow up 37±29 months. Between corrected ages 0-1 years, the mean spherical equivalent was +0.4±2.3 diopters; between ages 1-2, it was -1.3±3.2D; and ages 2-3 was -0.8±3.1D. Eyes that received more laser spots had significantly greater change in refractive error over time (0.30D more myopia per year per 1000 spots). None of the eyes with hyperopia before 18 months developed myopia during the follow-up period. CONCLUSIONS: Greater myopia developed over time in infants with ROP treated by laser to a larger area of avascular retina.

Tools for consensus analysis of experts' contours for radiotherapy structure definitions.

BACKGROUND AND PURPOSE: To demonstrate and examine the ability of a newly developed software tool to estimate and analyze consensus contours from manually created contours by expert radiation oncologists. MATERIAL AND METHODS: Several statistical methods and a graphical user interface were developed. For evaluation purposes, we used three breast cancer CT scans from the RTOG Breast Cancer Atlas Project. Specific structures were contoured before and after the experts' consensus panel meeting. Differences in the contours were evaluated qualitatively and quantitatively by the consensus software tool. Estimates of consensus contours were analyzed for the different structures and Dice-similarity and Dice-Jaccard coefficients were used for comparative evaluation. RESULTS: Based on kappa statistics, highest levels of agreement were seen in the left-breast, lumpectomy, and heart. Significant improvements between pre- and post-consensus contours were seen in delineation of the chestwall and breasts while significant variations were noticed in the supraclavicular and internal mammary nodes. Dice calculations for all pre-consensus STAPLE estimations and final consensus panel structures reached 0.80 or greater for the heart, left/right-breast, case-A lumpectomy, and chestwall. CONCLUSIONS: Using the consensus software tool incorporating STAPLE estimates provided the ability to create contours similar to the ones generated by expert physicians.