Rationale and Objectives
The aim of this study was to evaluate the usefulness of a novel computerized method for lung nodule detection on digital chest radiographs using temporal subtraction images.
Materials and Methods
To significantly reduce the number of false-positive results while maintaining high sensitivity, temporal subtraction images, which can enhance interval changes on sequential chest radiographs, were used. Fifty-one cases with lung nodules <3 cm and 51 cases without lung nodules were selected for an observer performance test. Twelve radiologists participated in this observer performance test. The radiologists’ performance was evaluated using receiver-operating characteristic analysis, on a continuous rating scale. To estimate the numbers of cases affected beneficially and those affected detrimentally using this computerized method, the computer output was assumed to have an effect on an observer’s diagnosis when there was a difference in rating score of ≥30% between the first and second ratings.
Results
The average area under the curve for all radiologists increased significantly from 0.849 to 0.950 with the computerized method ( P < .001). The mean number of cases affected beneficially was significantly higher than that of cases affected detrimentally (8.92 vs 1.25, P < .001).
Conclusions
The novel computerized method using temporal subtraction images would be useful in detecting lung nodules on digital chest radiographs.
Although chest radiography is the most prevalent screening procedure for detecting lung lesions, because it is economical and easy to use, radiologists are sometimes unaware of lung nodules that are perceptible on chest radiographs in retrospect , and it is even more difficult to detect small lung nodules . Computer-aided diagnosis (CAD) is now considered an approach that might improve the efficacy of radiologic image interpretation. For the detection of subtle lung nodules, however, CAD of a single chest radiograph suffers from interference by anatomic noise, leading to low specificity for an acceptable level of sensitivity . The temporal subtraction technique is a CAD method in which a previous chest radiograph is subtracted from a current radiograph so that interval changes are enhanced. Several studies have found that this technique improves the diagnostic accuracy of newly formed lung nodule . However, misregistration artifacts of this system, due to mismatching of normal anatomic structures in current and previous images, cause false-positive findings and sometimes lead to misinterpretation. In addition, both previous and current images are required for this CAD system, which may limit its clinical use.
We are developing a novel computerized method to assist radiologists in the detection of lung nodules by using temporal subtraction images. Our purpose in this study was to evaluate the usefulness of this computerized method on radiologists’ performance for lung nodule detection.
Materials and methods
Scanning Protocol
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Computerized Scheme
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Performance of the CAD System
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Observer Performance Study
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Statistical Analysis
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Results
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Table 1
Comparison of AUC Values without and with CAD
Observer AUC_P_ Without CAD With CAD Residents A 0.814 0.961 B 0.826 0.959 C 0.768 0.927 D 0.759 0.874 Average 0.792 0.930 <.001 Fellows E 0.853 0.951 F 0.895 0.952 G 0.799 0.937 H 0.812 0.934 Average 0.840 0.944 <.001 Attending radiologists I 0.931 0.982 J 0.903 0.970 K 0.933 0.972 L 0.900 0.979 Average 0.917 0.976 <.001 Average total 0.849 0.950 <.001
AUC, area under the curve; CAD, computer-aided diagnosis.
![Figure 3, Comparison of average receiver-operating characteristic curves of all radiologists for lung nodule detection without and with the computer-aided diagnosis (CAD) system. The average area under the curve (Az) for all radiologists increased significantly from 0.849 to 0.950 with the CAD system ( P < .001, Dorfman-Berbaum-Metz method [19] ).](https://storage.googleapis.com/dl.dentistrykey.com/clinical/UsefulnessofComputerizedMethodforLungNoduleDetectioninDigitalChestRadiographsUsingTemporalSubtractionImages/2_1s20S1076633211002078.jpg)
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Discussion
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