Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom

• Published in: PloS one Vol. 15; no. 5; p. e0232688
• Main Authors: Kang, Seongmin, Kim, Tae Hoon, Shin, Jae Min, Han, Kyunghwa, Kim, Ji Young, Min, Baeggi, Park, Chul Hwan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.05.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Biology and Life Sciences; Cancer research; Cancer screening; CAT scans; Chest; Computed tomography; Computer aided design; Computer aided medical diagnosis; Detection equipment; Diagnosis; Diagnostic equipment (Medical); Diagnostic imaging; Electric potential; Feasibility studies; Ghosts; Glass; Glass products; Image reconstruction; Iterative methods; Lung cancer; Lung diseases; Medical imaging; Medical prognosis; Medical schools; Medical screening; Medicine; Medicine and Health Sciences; Methods; Nodules; Opacity; Optimization; Physical Sciences; Radiology; Research and Analysis Methods; Screening; Technology application; Thickness; Tomography; Voltage; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0232688

This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom. A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and 400 mA; additionally, three slice thicknesses of 0.625, 1.25, and 2.5 mm and four reconstruction algorithms of adaptive statistical iterative reconstruction (ASIR-V) of 30, 60, and 90% were used. For each protocol, accuracy of the CAD system was evaluated for nine target GGNs of 6, 8, or 10 mm in size. The cut-off size was set to 5 mm to minimize false positives. Among the 180 combinations of tube voltage, tube current, slice thickness, and reconstruction algorithms, combination of 80 kVp, 200 mA, and 1.25-mm slice thickness with an ASIR-V of 90% had the best performance in the detection of GGNs with six true positives and no false positives. Other combinations had fewer than five true positives. In particular, any combinations with a 0.625-mm slice thickness had 0 true positive and at least one false positive result. Low-voltage chest CT with a thin slice thickness and a high iterative reconstruction algorithm improve the detection rate of GGNs with a CAD system in a phantom model, and may have potential in lung cancer screening.