News | February 12, 2015

Method fuses image features to detect subtle changes, generate prediction score

mammography, breast cancer, computer aided detection, image analysis

February 12, 2015 — Researchers at the University of Oklahoma have developed an image-analysis technique that is designed to improve breast cancer detection and diagnosis.

Bin Zheng, OU electrical and computer engineering professor and Oklahoma Tobacco Settlement Endowment Trust Cancer Research Scholar, and his research team have developed image processing algorithms to generate quantitative image markers by analyzing multiple digital X-ray images and building statistical data learning-based prediction models. The goal is to develop a new quantitative image analysis method that better predicts cancer risk or cancer prognosis, which ultimately leads to help establish more effective personalized cancer screening and treatment strategies.

For example, to improve efficacy of breast cancer screening, a number of breast cancer risk factors including age, breast density, family cancer history, lifestyle and test results on some common susceptible cancer gene mutations are reviewed. Using these risk factors, several lifetime breast cancer risk assessment models have been developed and applied in epidemiology studies.

“Our study is different. We do not intend to build another lifetime risk model to compete with the existing models. We focus on developing and testing a new risk model to predict whether a woman has high risk of developing breast cancer in a near-term after a negative screening mammography,” Zheng explained.

If successful, the model will help establish a new optimal personalized cancer screening model. As a result, an adaptively adjusted screening frequency and method can be applied to each woman at different time periods.

Zheng and his research team have been working to explore and identify image features and their difference, or asymmetry, between the left and right breasts. The images can be fused to build new risk models to more sensitively detect subtle image changes and/or abnormalities that are likely to lead to the development of mammography-detectable cancer in the next one to three years.

The team first identifies and computes useful image features from the two views of bilateral mammograms of the left and right breasts. Then they train statistical models (i.e., an artificial neural network) to generate a prediction score. The prediction score is the likelihood of a woman developing a “mammography-detectable” breast cancer after having a negative screening mammography examination, or classifying between malignant and benign recalls from suspicious mammograms detected by radiologists. 

The advanced prediction could help the medical community improve cancer screening efforts by focusing on women at greatest risk for developing breast cancer in the near-term and also reducing the number of women harmed from false-positive results. 

“The ultimate goal is to develop a personalized cancer screening,” Zheng explained. “Since cancer development is a progressive process, our new model focuses on detecting this dynamic process from the images and then improving the near-term breast cancer risk stratification among the women who participate in mammography-based breast cancer screening.”

As a result, only the small percentage of women stratified into the group of high risk in near-term should be more frequently screened, while the vast majority of women stratified at average or lower near-term cancer development risk could be screened at longer intervals – for example, every two to five years. This would increase cancer detection rate by focusing radiologists’ attention more on a small fraction of high-risk women by reducing the missed and/or overlooked subtle cancers, while also reducing the annual screening population and associated false-positive recalls among the vast majority of women with low near-term cancer risk.

For more information: www.ou.edu

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