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Proceedings Paper

Assessment of a quantitative mammographic imaging marker for breast cancer risk prediction
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Paper Abstract

The purpose of this study is to assess feasibility of applying a new quantitative mammographic imaging marker to predict short-term breast cancer risk. An image dataset involving 1,044 women was retrospectively assembled. Each woman had two sequential “current” and “prior” digital mammography screenings with a time interval from 12 to 18 months. All “prior” images were originally interpreted negative by radiologists. In “current” screenings, 402 women were diagnosed with breast cancer and 642 remained negative. There is no significant difference of BIRADS based mammographic density ratings between three case groups (p >0.6). A new computer-aided image processing scheme was applied to process negative mammograms acquired from the “prior” screenings and compute image features related to the bilateral mammographic density or tissue asymmetry between the left and right breasts. A group of 30 features related to GLCM texture features and a conventional computer-aided detection scheme generated results are extracted from both CC and MLO views. Using a leave-one-case-out cross-validation method, a support vector machine model was developed to produce a new quantitative imaging marker to predict the likelihood of a woman having mammography-detectable cancer in the next subsequent (“current”) screening. When applying the model to classify between 402 positive and 642 negative cases, area under a ROC curve is 0.70−0.02 and the odds ratios is 6.93 with 95% confidence interval of [4.80,10.01]. This study demonstrated feasibility of applying a quantitative imaging marker to predict short-term cancer risk, which aims to help establish a new paradigm of personalized breast cancer screening.

Paper Details

Date Published: 4 March 2019
PDF: 7 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520X (4 March 2019); doi: 10.1117/12.2512802
Show Author Affiliations
Morteza Heidari, Univ. of Oklahoma (United States)
Seyedehnafiseh Mirniaharikandehei, Univ. of Oklahoma (United States)
Abolfazl Zargari Khuzani, Univ. of Oklahoma (United States)
Wei Qian, Univ. of Texas at El Paso (United States)
Yuchen Qiu, Univ. of Oklahoma (United States)
Bin Zheng, Univ. of Oklahoma (United States)


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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