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Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms?
Author(s): Ziba Gandomkar; Ernest U. Ekpo; Sarah J. Lewis; Karla K. Evans; Kriscia A. Tapia; PhuongDung Trieu; Jeremy M. Wolfe; Patrick C. Brennan
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Paper Abstract

This study measured the correlation between the magnitude of the presence of the abnormality gist and case difficulty based on standard presentation and reporting mechanisms for 80 cases. Half of the cases contained biopsy-proven cancer while the remainder were normal and confirmed to be cancer-free for at least two years of follow-up. In the gist experiment, seventeen breast radiologists and physicians gave an abnormality score on a scale from 0 (confident normal) to 100 (confident abnormal) to unilateral CC mammograms following a very brief, 500 millisecond presentation of the image. Independently, each mammogram was assessed by a separate sample of at least 40 radiologists using standard presentation and reporting mechanisms, with these readers asked to locate any cancers present. All readers reported at least 1000 cases annually. For each case and each category, the percentage of correct reports served as an objective measure of case difficulty (lower rate of correct report shows a more difficult case). For each of the 17 readers, the association between the abnormality scores from the gist study and detection rates from the earlier reports was examined using Spearman correlation. None of the coefficients were significantly different from zero (p<0.05). For the normal cases, the correlation coefficient between abnormality scores and detection rates for the 17 readers ranged from -0.262 to 0.258, and for cancer -0.180 to 0.309. The results suggest that the gist signal may indicate the presence of cancer, using mechanisms other than those employed in usual reporting, and might be exploited to improve breast cancer detection.

Paper Details

Date Published: 4 March 2019
PDF: 8 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 1095203 (4 March 2019); doi: 10.1117/12.2513151
Show Author Affiliations
Ziba Gandomkar, The Univ. of Sydney (Australia)
Ernest U. Ekpo, The Univ. of Sydney (Australia)
Sarah J. Lewis, The Univ. of Sydney (Australia)
Karla K. Evans, Univ. of York (United Kingdom)
Kriscia A. Tapia, The Univ. of Sydney (Australia)
PhuongDung Trieu, The Univ. of Sydney (Australia)
Jeremy M. Wolfe, Harvard Medical School (United States)
Patrick C. Brennan, The Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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