Back to Search
Start Over
Computer-assisted diagnosis for an early identification of lung cancer in chest X rays.
- Source :
-
Scientific reports [Sci Rep] 2023 May 12; Vol. 13 (1), pp. 7720. Date of Electronic Publication: 2023 May 12. - Publication Year :
- 2023
-
Abstract
- Computer-assisted diagnosis (CAD) algorithms have shown its usefulness for the identification of pulmonary nodules in chest x-rays, but its capability to diagnose lung cancer (LC) is unknown. A CAD algorithm for the identification of pulmonary nodules was created and used on a retrospective cohort of patients with x-rays performed in 2008 and not examined by a radiologist when obtained. X-rays were sorted according to the probability of pulmonary nodule, read by a radiologist and the evolution for the following three years was assessed. The CAD algorithm sorted 20,303 x-rays and defined four subgroups with 250 images each (percentiles ≥ 98, 66, 33 and 0). Fifty-eight pulmonary nodules were identified in the ≥ 98 percentile (23,2%), while only 64 were found in lower percentiles (8,5%) (p < 0.001). A pulmonary nodule was confirmed by the radiologist in 39 out of 173 patients in the high-probability group who had follow-up information (22.5%), and in 5 of them a LC was diagnosed with a delay of 11 months (12.8%). In one quarter of the chest x-rays considered as high-probability for pulmonary nodule by a CAD algorithm, the finding is confirmed and corresponds to an undiagnosed LC in one tenth of the cases.<br /> (© 2023. The Author(s).)
- Subjects :
- Humans
X-Rays
Tomography, X-Ray Computed methods
Retrospective Studies
Radiographic Image Interpretation, Computer-Assisted methods
Sensitivity and Specificity
Diagnosis, Computer-Assisted methods
Solitary Pulmonary Nodule diagnostic imaging
Lung Neoplasms diagnostic imaging
Multiple Pulmonary Nodules diagnostic imaging
Subjects
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 13
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 37173327
- Full Text :
- https://doi.org/10.1038/s41598-023-34835-z