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Proceedings Paper

Application of computer-extracted breast tissue texture features in predicting false-positive recalls from screening mammography
Author(s): Shonket Ray; Jae Y. Choi; Brad M. Keller; Jinbo Chen; Emily F. Conant; Despina Kontos
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Paper Abstract

Mammographic texture features have been shown to have value in breast cancer risk assessment. Previous models have also been developed that use computer-extracted mammographic features of breast tissue complexity to predict the risk of false-positive (FP) recall from breast cancer screening with digital mammography. This work details a novel locallyadaptive parenchymal texture analysis algorithm that identifies and extracts mammographic features of local parenchymal tissue complexity potentially relevant for false-positive biopsy prediction. This algorithm has two important aspects: (1) the adaptive nature of automatically determining an optimal number of region-of-interests (ROIs) in the image and each ROI’s corresponding size based on the parenchymal tissue distribution over the whole breast region and (2) characterizing both the local and global mammographic appearances of the parenchymal tissue that could provide more discriminative information for FP biopsy risk prediction. Preliminary results show that this locallyadaptive texture analysis algorithm, in conjunction with logistic regression, can predict the likelihood of false-positive biopsy with an ROC performance value of AUC=0.92 (p<0.001) with a 95% confidence interval [0.77, 0.94]. Significant texture feature predictors (p<0.05) included contrast, sum variance and difference average. Sensitivity for false-positives was 51% at the 100% cancer detection operating point. Although preliminary, clinical implications of using prediction models incorporating these texture features may include the future development of better tools and guidelines regarding personalized breast cancer screening recommendations. Further studies are warranted to prospectively validate our findings in larger screening populations and evaluate their clinical utility.

Paper Details

Date Published: 20 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351X (20 March 2014); doi: 10.1117/12.2043742
Show Author Affiliations
Shonket Ray, Univ. of Pennsylvania (United States)
Jae Y. Choi, Univ. of Pennsylvania (United States)
Brad M. Keller, Univ. of Pennsylvania (United States)
Jinbo Chen, Univ. of Pennsylvania (United States)
Emily F. Conant, Univ. of Pennsylvania (United States)
Despina Kontos, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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