Comparison of two reader modes of computer-aided diagnosis in lung nodules on low-dose chest CT scan

Abstract

Low-dose computerized tomography (LDCT) scanning is of great significance for monitoring and management of pulmonary nodules on chest computerized tomography (CT). Nevertheless, the malignant potential of these nodules is often difficult to detect, especially for some smaller pulmonary nodules on LDCT images. Recent advances using the state-of-art computer-aided detection (CAD) system have attempted to address this problem by identifying small nodules that can be easily missed during clinical practice. CAD is used in two reading modes: Concurrent-reader (CR) mode or second-reader (SR) mode. In this study, we prospectively evaluated the efficiency of a CAD system’s SR and CR modes in detecting pulmonary nodules on LDCT. We found that the SR mode improves pulmonary nodule detection regardless of the dose and experience level, especially for interns in the low-dose setting. The CR mode maintains the sensitivity of SR mode while significantly decreasing reading times.

Keywords

• Computed tomography
• radiation dosage
• pulmonary nodules
• computer-assisted
• deep learning