• The Use of Computer-Aided Detection for the Assessment of Pulmonary Arterial Filling Defects at Computed Tomographic Angiography

    j Comput Assist Tomogr • Volume 32, Number 6, November/December 2008

    Anna C. Walsham, MD, Heidi C. Roberts, FRCP(C), MD, Hany Mehdizadeh Kashani, MD, Christopher N. Mongiardi, MD, Yuen-Li Ng, MD, and Demetris A. Patsios, MD

    Purpose: To validate a computer-aided detection (CAD) tool for the detection of pulmonary arterial filling defects at computed tomo­graphic pulmonary angiography (CTPA) and to assess its benefit for readers of different levels of experience.


    Methods: One hundred consecutive CTPA studies were retro­spectively evaluated by a chest radiologist for presence of emboli, serving as the reference standard. Subsequently, examinations were analyzed using commercially available second-generation CAD software (ImageChecker CT, version 2.1; R2 Technology, Inc., Sunnyvale, Calif). The staff radiologist assessed all CAD marks and classified them as true positive or false positive (FP), and any unmarked emboli were classified as false negative. Computer-aided detection software was also evaluated on a case basis compared with the reference standard.
    For the second part of the study, the 100 CTPAs were reviewed by 3 additional readers of different levels of experience, both without and with CAD, and findings correlated with the reference standard.

    Results: Twenty-one studies (21%) were positive for pulmonary embolism. Of these, 18 were true positive on a case basis, and 3 were false negative. Of the 79 negative studies, 16 were true negative with no CAD marks, and the remaining 63 were FP. On a case basis, CAD sensitivity was 86%, specificity was 20%, negative predictive value was 84%, and positive predictive value (PPV) was 22%. Overall, the CAD software yielded 318 marks, identifying 64 of 93 emboli with an additional 254 FP marks. On a mark basis, sensitivity was 69%, and PPV was 20%.
    Computer-aided detection did not influence the most experienced reader (a chest fellow). Although CAD improved the subjective confidence of the second-year resident in some cases, it had no influence on overall interpretation or accuracy. Computer-aided detection improved accuracy only for the most inexperienced reader, helping this reader to identify 9 emboli not initially appreciated.

    Conclusions: Computer-aided detection specificity and PPV are poor due to expected FP marks, although, often, these can be easily dismissed. However, CAD software may play an important role as a second reader for residents or inexperienced readers.