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. 2020 May 22;15(5):e0232688.
doi: 10.1371/journal.pone.0232688. eCollection 2020.

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

Seongmin Kang  1 , Tae Hoon Kim  1 , Jae Min Shin  1 , Kyunghwa Han  2 , Ji Young Kim  1 , Baeggi Min  3 , Chul Hwan Park  1
Affiliations

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

Seongmin Kang et al. PLoS One. .

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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Conflict of interest statement

There are no relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc. Baeggi Min is an employee of GE healthcare, Korea, and received support in the form of salary from GE healthcare. However he is not relevant to the funder. This study does not have a commercial affiliation with GE. Baggie Min is a CT application specialist and expert in CAD programs, and contributed to data collection, image acquisition and image analysis. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Nine target ground glass nodules (GGNs) in the left lung of lung screening phantom.
A lung screening computed tomography phantom was used. Attenuation of the lung parenchyma was −900 Hounsfield unit. Of the 15 artificial GGNs in the left lung, nine lesions (6-, 8-, or 10-mm nodules at the lung apex, tracheal bifurcation, and base of the lung) were used as targets for the computer-assisted detection system.
Fig 2
Fig 2. Example of true positive detection.
Fig 3
Fig 3. Example of false positive detection.
See this image and copyright information in PMC

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