Abstract
Detection of small pulmonary nodules is the goal of lung cancer screening. Computer-aided detection (CAD) systems are recommended to use in lung cancer computed tomography (CT) screening to increase the accuracy of nodule detection. Size and density of lung nodules are primary factors in determining the risk of malignancy. Therefore, purpose of this study is to apply computer-simulated virtual nodules based on the point spread function (PSF) measured in same scanner (maintaining spatial resolution condition) to assess the CAD system performance dependence on nodule size and density. Virtual nodules with density differences between lung background and nodule density (ΔCT) values (200, 300 and 400 HU) and different sizes (4 to 8 mm) were generated and fused on clinical images. CAD detection was performed and free-response receiver operating characteristic (FROC) curves were obtained. Results show that both density and size of virtual nodules can affect detection efficiency. Detailed results are possible to use for quantitative analysis of a CAD system performance. This study suggests that PSF-based virtual nodules could be effectively used to assess the lung cancer CT screening CAD system performance dependence on nodule size and density.
Highlights
The National Cancer Institute (USA) has released the results of the National Lung Screening Trial, which has identified that computed tomography (CT) screening can reduce the mortality from lung cancer 20% more than by screening with chest radiography [1]
The purpose of this study was to apply the virtual nodules to demonstrate the dependence of the computer-aided detection (CAD) performance on nodule size and density with the free-response receiver operating characteristic (FROC) analysis
Performance dependence of a lung cancer CT screening CAD system has been assessed based on two nodule characteristics of size and density
Summary
As the image databases included actual nodules with complicated shapes and heterogeneous density, the CAD performance evaluated with using the database was considered as an overall performance for various size and density of nodules, not for specific size/density. Because actual nodules are complicated, typically having varied morphologies and heterogeneous densities, the image databases do not lend themselves for classification of nodules by their size and density. For this task, artificial nodules with known size and density included in a lung phantom might be useful [15], more clinical evaluations using lung images obtained from CT screening are essential
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