Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans

To implement a cost-effective low-dose computed tomography (LDCT) lung cancer screening program at the population level, accurate and efficient interpretation of a large volume of LDCT scans is needed. The objective of this study was to evaluate a workflow strategy to identify abnormal LDCT scans in which a technician assisted by computer vision (CV) software acts as a first reader with the aim to improve speed, consistency, and quality of scan interpretation.

Without knowledge of the diagnosis, a technician reviewed 828 randomly batched scans (136 with lung cancers, 556 with benign nodules, and 136 without nodules) from the baseline Pan-Canadian Early Detection of Lung Cancer Study that had been annotated by the CV software CIRRUS Lung Screening (Diagnostic Image Analysis Group, Nijmegen, The Netherlands). The scans were classified as either normal (no nodules ≥1 mm or benign nodules) or abnormal (nodules or other abnormality). The results were compared with the diagnostic interpretation by Pan-Canadian Early Detection of Lung Cancer Study radiologists.

The overall sensitivity and specificity of the technician in identifying an abnormal scan were 97.8% (95% confidence interval: 96.4-98.8) and 98.0% (95% confidence interval: 89.5-99.7), respectively. Of the 112 prevalent nodules that were found to be malignant in follow-up, 92.9% were correctly identified by the technician plus CV compared with 84.8% by the study radiologists. The average time taken by the technician to review a scan after CV processing was 208 ± 120 seconds.

Prescreening CV software and a technician as first reader is a promising strategy for improving the consistency and quality of screening interpretation of LDCT scans.