News | February 12, 2015

Researcher Improves Near-Term Breast Cancer Detection With Image Analysis

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:

Related Content

Illinois Governor Approves State Breast Density Reporting Bill Into Law
News | Breast Density | August 13, 2018
Illinois Gov. Bruce Rauner approved the Illinois Breast Density Reporting Law (Public Act 100-0749) on Aug. 10, 2018...
PET Tracer Identifies Estrogen Receptor Expression Differences in Breast Cancer Patients
News | PET Imaging | August 09, 2018
In metastatic breast cancer, prognosis and treatment is largely influenced by estrogen receptor (ER) expression of the...
iCAD Receives FDA Clearance of PowerLook Density Assessment for Digital Breast Tomosynthesis
Technology | Breast Density | August 08, 2018
iCAD announced U.S. Food and Drug Administration (FDA) clearance of its latest artificial intelligence (AI) software...
Cardiac Imaging Reveals Roots of Preeclampsia Damage in Pregnant Women
News | Women's Health | August 07, 2018
Johns Hopkins researchers say a heart imaging study of scores of pregnant women with the most severe and dangerous form...
Cardiac Monitoring a Higher Priority for High-Risk Breast Cancer Patients
News | Cardio-oncology | August 07, 2018
August 7, 2018 — While heart failure is an uncommon complication of...
Hologic Acquires Digital Specimen Radiography Company Faxitron Bioptics

VisionCT 3-D breast specimen-designated computed tomography (CT) system. Image courtesy of Faxitron Bioptics.

News | Breast Imaging | July 31, 2018
Hologic Inc. announced it has completed the acquisition of Faxitron Bioptics, a privately-held leader in digital...
Konica Minolta Hosting Lunch and Learn at 23rd Annual Mammography Meeting in Santa Fe
News | Breast Imaging | July 31, 2018
Konica Minolta Healthcare Americas Inc. will sponsor a lunch and learn featuring its Exa Mammo platform during the 23rd...
FDA Approves New Tomosynthesis Quality Control Tests for ACR Digital Mammography QC Manual
News | Mammography | July 30, 2018
The U.S. Food and Drug Administration (FDA) recently approved the American College of Radiology’s (ACR’s) amendment to...
The Magtrace and Sentimag Magnetic Localization System uses magnetic detection during sentinel lymph node biopsy procedures to identify specific lymph nodes, known as sentinel lymph nodes, for surgical removal. The FDA granted approval of the Sentimag System to Endomagnetics Inc.

The  Endomagnetics' Magtrace and Sentimag Magnetic Localization System uses magnetic detection during sentinel lymph node biopsy procedures to identify specific lymph nodes, known as sentinel lymph nodes, for surgical removal.

Technology | Women's Health | July 24, 2018
July 24, 2018 — The U.S.
Overlay Init