Inter-observer variability between readers of CT images: all for one and one for all

Abstract

BACKGROUND: The markup of medical image datasets is based on the subjective interpretation of the observed entities by radiologists. There is currently no widely accepted protocol for determining ground truth based on radiologists reports. AIM: To assess the accuracy of radiologist interpretations and their agreement for the publicly available dataset CTLungCa-500, as well as the relationship between these parameters and the number of independent readers of CT scans. MATERIALS AND METHODS: Thirty-four radiologists took part in the dataset markup. The dataset included 536 patients who were at high risk of developing lung cancer. For each scan, six radiologists worked independently to create a report. After that, an arbitrator reviewed the lesions discovered by them. The number of true-positive, false-positive, true-negative, and false-negative findings was calculated for each reader to assess diagnostic accuracy. Further, the inter-observer variability was analyzed using the percentage agreement metric. RESULTS: An increase in the number of independent readers providing CT scan interpretations leads to accuracy increase associated with a decrease in agreement. The majority of disagreements were associated with the presence of a lung nodule in a specific site of the CT scan. CONCLUSION: If arbitration is provided, an increase in the number of independent initial readers can improve their combined accuracy. The experience and diagnostic accuracy of individual readers have no bearing on the quality of a crowd-tagging annotation. At four independent readings per CT scan, the optimal balance of markup accuracy and cost was achieved.