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ORIGINAL ARTICLE
Year : 2017  |  Volume : 7  |  Issue : 1  |  Page : 8

Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph


1 Department of Experimental Radiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich-Schiller University, Jena; Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany
2 Department of Experimental Radiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany
3 Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany

Correspondence Address:
Ismini E Papageorgiou
Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Dr.-Robert-Koch Str. 39, 99734 Nordhausen
Germany
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcis.JCIS_75_16

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Aim: The most ubiquitous chest diagnostic method is the chest radiograph. A common radiographic finding, quite often incidental, is the nodular pulmonary lesion. The detection of small lesions out of complex parenchymal structure is a daily clinical challenge. In this study, we investigate the efficacy of the computer-aided detection (CAD) software package SoftView™ 2.4A for bone suppression and OnGuard™ 5.2 (Riverain Technologies, Miamisburg, OH, USA) for automated detection of pulmonary nodules in chest radiographs. Subjects and Methods: We retrospectively evaluated a dataset of 100 posteroanterior chest radiographs with pulmonary nodular lesions ranging from 5 to 85 mm. All nodules were confirmed with a consecutive computed tomography scan and histologically classified as 75% malignant. The number of detected lesions by observation in unprocessed images was compared to the number and dignity of CAD-detected lesions in bone-suppressed images (BSIs). Results: SoftView™ BSI does not affect the objective lesion-to-background contrast. OnGuard™ has a stand-alone sensitivity of 62% and specificity of 58% for nodular lesion detection in chest radiographs. The false positive rate is 0.88/image and the false negative (FN) rate is 0.35/image. From the true positive lesions, 20% were proven benign and 80% were malignant. FN lesions were 47% benign and 53% malignant. Conclusion: We conclude that CAD does not qualify for a stand-alone standard of diagnosis. The use of CAD accompanied with a critical radiological assessment of the software suggested pattern appears more realistic. Accordingly, it is essential to focus on studies assessing the quality-time-cost profile of real-time (as opposed to retrospective) CAD implementation in clinical diagnostics.


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