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Proceedings Paper

Evaluation of an interactive computer-aided diagnosis (ICAD) system for mammography: a pilot study
Author(s): Bin Zheng; Gordon Abrams; Cynthia A. Britton M.D.; Christiane M. Hakim; Amy Lu; Ronald J. Clearfield M.D.; John Drescher; Glenn S. Maitz; David Gur
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Paper Abstract

Five radiologists detected suspicious mass regions depicted on mammograms acquired from 32 examinations during this pilot study. Among these, 24 examinations depicted subtle masses (12 malignant and 12 benign) and 8 were negative. Each observer interpreted a case in a sequential order under three reading modes. In mode one, the observer interpreted images without viewing CAD-generated cues. The observer provided two likelihood scores (for detection and classification) for each identified suspicious region. In mode two, CAD-cued results were provided and the observer could decide whether to make any changes in the previous ratings. In mode three, each observer was forced to query at least one suspected region. Once a region was queried, CAD scheme automatically segmented the mass region and computed a set of image features. Using a conditioned k-nearest neighbor (KNN) algorithm, six reference regions that were considered "the most similar" to the queried region were selected and displayed along with CAD-generated scores. Again, the observer had an option to change previous ratings. Experimental results were analyzed using ROC method. Five observers marked total 271, 276, and 281 mass regions under the three reading modes, respectively. In mode 2 observers marked 5 new suspected mass regions and did not make any changes in previously rated detection or classification scores. In mode three, although observers queried 18 additional regions, 13 were discarded and 5 were marked with region specific related scores. The observers also changed previous rating scores of 28 mass regions marked during mode one. The areas under ROC curves for individual readers ranged from 0.51 to 0.71 for mass detection (p = 0.67) and from 0.50 to 0.73 for mass classification (p = 0.43). This pilot study suggested that using ICAD could increase radiologists' confidence in their decision making. We also found that because radiologists tend to accept a higher false-positive rate in a laboratory environment, once they made their detection decision during the initial reading, they are frequently reluctant to make changes during the following modes. Hence, while simple and efficient operationally, the sequential reading mode may not be an optimal approach to evaluate the actual utility of ICAD.

Paper Details

Date Published: 8 March 2007
PDF: 8 pages
Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65151M (8 March 2007); doi: 10.1117/12.705756
Show Author Affiliations
Bin Zheng, Univ. of Pittsburgh (United States)
Gordon Abrams, Univ. of Pittsburgh (United States)
Cynthia A. Britton M.D., Univ. of Pittsburgh (United States)
Christiane M. Hakim, Univ. of Pittsburgh (United States)
Amy Lu, Univ. of Pittsburgh (United States)
Ronald J. Clearfield M.D., Univ. of Pittsburgh (United States)
John Drescher, Univ. of Pittsburgh (United States)
Glenn S. Maitz, Univ. of Pittsburgh (United States)
David Gur, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 6515:
Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Berkman Sahiner, Editor(s)

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