Early detection of lung cancer using ultra-low-dose computed tomography in coronary CT angiography scans among patients with suspected coronary heart disease
- PMID: 29173759
- DOI: 10.1016/j.lungcan.2017.10.004
Early detection of lung cancer using ultra-low-dose computed tomography in coronary CT angiography scans among patients with suspected coronary heart disease
Abstract
Objectives: To assess whether an additional chest ultra-low-dose CT scan to the coronary CT angiography protocol can be used for lung cancer screening among patients with suspected coronary artery disease.
Methods: 175 patients underwent coronary CT angiography for assessment of coronary artery disease, additionally undergoing ultra-low-dose CT screening to early diagnosis of lung cancer in the same scanner (80kVp and 15mAs). Patients presenting pulmonary nodules were followed-up for two years, repeating low-dose CTs in intervals of 3, 6, or 12 months based on nodule size and growth rate in accordance with National Comprehensive Cancer Network guidelines.
Results: Ultra-low-dose CT identified 71 patients with solitary pulmonary nodules (41%), with a mean diameter of 5.50±4.00mm. Twenty-eight were >6mm, and in 79% (n=22) of these cases they were false positive findings, further confirmed by follow-up (n=20), resection (n=1), or biopsy (n=1). Lung cancer was detected in six patients due to CT screening (diagnostic yield: 3%). Among these, four cases could not be detected in the cardiac field of view. Most patients were in early stages of the disease. Two patients diagnosed at advanced stages died due to cancer complications. The addition of the ultra-low-dose CT scan represented a radiation dose increment of 1.22±0.53% (effective dose, 0.11±0.03mSv).
Conclusions: Lung cancer might be detected using additional ultra-low-dose protocols in coronary CT angiography scans among patients with suspected coronary artery disease.
Keywords: Cardiac imaging techniques; Cardiovascular diseases; Lung neoplasms; Mass screening; Tomography; X-ray computed.
Copyright © 2017 Elsevier B.V. All rights reserved.
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