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Can computer assisted diagnosis (CAD) be used as a screening tool in the detection of pulmonary nodules when using 64-slice multidetector computed tomography?

Authors Haider Z, Idris M, Memon WA, Kashif N, Idris SM, Sajjad Z, Saeed

Published 6 December 2011 Volume 2011:4 Pages 815—819


Review by Single-blind

Peer reviewer comments 2

Zishan Haider1, Muhammad Idris1, Wasim A Memon1, Nazia Kashif1, Sidra Idris1, Zafar Sajjad1, Saeed Akram2
1Department of Radiology, Aga Khan University Hospital, Karachi, Pakistan; 2Medicine Department, Aga Khan University Hospital, Karachi, Pakistan

Objectives: To evaluate (1) whether or not the addition of computer-assisted diagnosis (CAD) to 64-slice multidetector computed tomography (CT) can be used as a screening tool for detection of pulmonary nodules in routine CT chest examinations and (2) whether or not to advocate the incorporation of CAD as a screening tool into our daily practice.
Materials and methods: A retrospective cross-sectional analysis of 109 consecutive patients who had all undergone routine contrast-enhanced CT chest examinations for indications other than lung cancer at the Radiology Department of Aga Khan University Hospital, Karachi, between November 2010 and January 2011. All examinations were evaluated in terms of the detection of pulmonary nodules by a consultant radiologist and CAD (ImageChecker CT Algorithm R2 Technology) software. The ability of CAD software to detect pulmonary nodules was evaluated against the reference standard. In addition, a chest radiologist also calculated the number of pulmonary nodules. The sensitivity and specificity of the CAD software were calculated against the reference standard by using a 2 × 2 table. The Mann-Whitney U test was applied to compare the performances of CAD and the radiologist.
Results: CAD detected 610 pulmonary nodules while the radiologist detected only 113. The reference standard declared 198 pulmonary nodules to be true nodules. CAD detected 95% of all true nodules (189/198), whereas the radiologist detected only 57% (113/198). In the detection of true pulmonary nodules, CAD had 98% sensitivity compared with the radiologist who had 57% sensitivity; the statistical difference between their performances had a P value <0.001.
Conclusion: Considering the high sensitivity of CAD to detect nearly all true pulmonary nodules, we advocate its application as a screening tool in all CT chest examinations for the early detection of pulmonary nodules and lung carcinoma.

Keywords: CT chest examinations, pulmonary nodules, lung carcinoma, computer-assisted diagnosis

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