Prospective Validation of Computer-Assisted Detection of Pulmonary Nodules and Lung Cancer in Chest X-Rays

Background: Computer-aided diagnosis (CAD) algorithms may be useful for the detection of pulmonary nodules, some of them early-stage primary lung cancers (LCs). Method: A CAD algorithm for the identification of pulmonary nodules in chest X-rays was used in a prospective cohort of patients without respiratory symptoms who underwent an X-ray. Chest X-rays were sorted according to the probability of the presence of a pulmonary nodule, and images placed in the higher percentiles by the algorithm were read by a radiologist, checked for previous diagnoses, and assessed for new primary LCs. Results: The radiological study confirmed the presence of a pulmonary nodules in 88 out of the 1842 chest X-rays placed ≥70th percentile (4.8%, 95% confidence interval 3.8–5.7%), 44 of which were found to be malignant (50%), and seven of them undiagnosed primary LCs (7.9%). The CAD algorithm prompted a new diagnosis of primary LC in 0.4% (95% confidence interval 0.1–0.7%), of the high-probability chest X-rays. Conclusions: A CAD algorithm prospectively identified pulmonary nodules among patients without respiratory symptoms. Radiologist-confirmed round opacities were found in 4.8% of the high-probability images, and previously undiagnosed primary lung cancers accounted for near 8% of confirmed pulmonary nodules.