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Proceedings Paper

ROC study of the effects of computer-aided interval change analysis on radiologists' characterization of breast masses in two-view serial mammograms
Author(s): Lubomir M. Hadjiiski; Mark A. Helvie; Berkman Sahiner; Heang-Ping Chan; Marilyn A. Roubidoux; Alexis Nees; Nicholas Petrick; Caroline Blane; Chintana Paramagul; Janet E. Bailey; Stephanie Patterson; Katherine Klein; Dorit Adler; Michelle Foster; Joseph Shen
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Paper Abstract

We have previously evaluated the effects of computer-aided diagnosis (CAD) on radiologists' characterization of malignant and benign breast masses in single-view serial mammograms. In this study, we conducted observer performance experiments with ROC methodology in which the radiologists read the serial mammograms in two-views (CC and MLO) without and with CAD. 47 temporal pairs of two-view serial mammograms (27 malignant and 20 benign) containing masses were chosen from 39 patient files and digitized. The corresponding masses on each temporal pair were analyzed by the CAD program. For this data set, the computer classifier achieved a test Az value of 0.90. Five MQSA radiologists assessed the two-view temporal pairs and provided estimates of the likelihood of malignancy without and then with CAD. For the five radiologists, the average Az was 0.81 (range: 0.72-0.88) without CAD and improved to 0.88 (range: 0.86-0.90) with CAD. The improvement was statistically significant (p=0.038). In comparison, the test Az value of the computer classifier for single view analysis was 0.87. The average Az of the radiologists for reading the single view temporal pairs without CAD was 0.78 (range: 0.73-0.83) and was improved significantly (p=0.002) to 0.84 (range: 0.77-0.88) with CAD. CAD using interval change analysis can significantly improve radiologists' accuracy in classification of masses. Classification based on information from two-views is more accurate than that based on single view for both the radiologists and the computer classifier. CAD can further improve radiologists' performance even in two-view reading.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535892
Show Author Affiliations
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Marilyn A. Roubidoux, Univ. of Michigan (United States)
Alexis Nees, Univ. of Michigan (United States)
Nicholas Petrick, FDA Ctr. for Devices and Radiological Health (United States)
Caroline Blane, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Janet E. Bailey, Univ. of Michigan (United States)
Stephanie Patterson, Univ. of Michigan (United States)
Katherine Klein, Univ. of Michigan (United States)
Dorit Adler, Univ. of Michigan (United States)
Michelle Foster, Univ. of Michigan (United States)
Joseph Shen, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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