News | Artificial Intelligence | May 08, 2019

Novel Artificial Intelligence Method Predicts Future Risk of Breast Cancer

AI model combines traditional risk factors with breast density and other information from the mammogram to enhance risk prediction

Novel Artificial Intelligence Method Predicts Future Risk of Breast Cancer

May 8, 2019 — Researchers from two major institutions have developed a new tool with advanced artificial intelligence (AI) methods to predict a woman's future risk of breast cancer, according to a new study published in the journal Radiology.1

Identifying women at risk for breast cancer is a critical component of effective early disease detection. However, available models that use factors such as family history and genetics fall far short in predicting an individual woman's likelihood of being diagnosed with the disease.

Breast density — the amount of dense tissue compared to the amount of fatty tissue in the breast on a mammogram — is an independent risk factor for breast cancer that has been added to some models to improve risk assessment. It is based on subjective assessment that can vary across radiologists, so deep learning, a subset of AI in which computers learn by example, has been studied as a way to standardize and automate these measurements.

"There's much more information in a mammogram than just the four categories of breast density," said study lead author Adam Yala, Ph.D. candidate at the Massachusetts Institute of Technology (MIT) in Cambridge, Mass. "By using the deep learning model, we learn subtle cues that are indicative of future cancer."

Yala, in collaboration with Regina Barzilay, Ph.D., an AI expert and professor at MIT, and Constance Lehman, M.D., Ph.D., chief of breast imaging at Massachusetts General Hospital (MGH) in Boston and professor of radiology at Harvard Medical School, recently compared three different risk assessment approaches. The first model relied on traditional risk factors, the second on deep learning that used the mammogram alone, and the third on a hybrid approach that incorporated both the mammogram and traditional risk factors into the deep learning model.

The researchers used almost 90,000 full-resolution screening mammograms from about 40,000 women to train, validate and test the deep learning model. They were able to obtain cancer outcomes through linkage to a regional tumor registry.

The deep learning models yielded substantially improved risk discrimination over the Tyrer-Cuzick model, a current clinical standard that uses breast density in factoring risk. When comparing the hybrid deep learning model against breast density, the researchers found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk. The advantages held across different subgroups of women.

"Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories," Barzilay said. "Until now, African-American women were at a distinct disadvantage in having accurate risk assessment of future breast cancer. Our AI model has changed that."

"There's a very large amount of information in a full-resolution mammogram that breast cancer risk models have not been able to use until recently," Yala added. "Using deep learning, we can learn to leverage that information directly from the data and create models that are significantly more accurate across diverse populations."

AI-assisted breast density measurements are already in use for screening mammograms performed at MGH. The researchers are tracking its performance in the clinic while working on refining the ways to communicate risk information to women and their primary care doctors.

"A missing element to support more effective, more personalized screening programs has been risk assessment tools that are easy to implement and that work across the full diversity of women whom we serve," Lehman said. "We are thrilled with our results and eager to work closely with our healthcare systems, our providers and, most importantly, our patients to incorporate this discovery into improved outcomes for all women."

For more information: www.pubs.rsna.org/journal/radiology

 

Reference

1. Yala A., Lehman C., Schuster T., et al. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology, May 7, 2019. https://doi.org/10.1148/radiol.2019182716

Related Content

Results of Journal of the American College of Surgeons study should reassure breast cancer patients who experienced surgical postponements due to COVID-19 pandemic

Association between Time to Operation and Pathological Stage in DCIS & ER+ Breast Cancer. Courtesy of American College of Surgeons

News | Women's Health | August 07, 2020
August 7, 2020 — A new br...
Collaboration will include data sharing, R&D and an upgrade of RadNet’s fleet of mammography systems to Hologic’s state-of-the-art imaging technology
News | Breast Imaging | August 06, 2020
August 6, 2020 — RadNet, Inc., a national leader in providing hig
Hologic, Inc. launched the Back to Screening campaign encouraging women to schedule their annual mammograms now that healthcare facilities across the nation are re-opening their doors following closures due to the COVID-19 pandemic.

Nine-time GRAMMY Award winner and breast cancer survivor Sheryl Crow has served as the spokesperson for Hologic’s Genius 3D Mammography exam for nearly five years.

News | Breast Imaging | August 03, 2020
August 3, 2020 — Hologic, Inc. launched the Back to Screening campaign encouraging women to schedule their ann
It covers every major modality, including breast imaging/mammography, fixed and portable C-arms (cath, IR/angio, hybrid, OR), CT, MRI, nuclear medicine, radiographic fluoroscopy, ultrasound and X-ray
News | Radiology Imaging | July 29, 2020
July 29, 2020 — IMV Medical Information, part of Scien...
Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. Image courtesy of Ibex Medical Analytics

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. Image courtesy of Ibex Medical Analytics

News | Prostate Cancer | July 28, 2020
July 28, 2020 — A study published in 
Zebra Medical Vision announced its sixth FDA 510(k) clearance for its mammography solution, HealthMammo, which has already received a CE mark. Zebra Medical’s algorithm empowers breast radiologists by prioritizing and identifying suspicious mammograms, providing a safety net for radiologists. The suspicious mammograms are identified faster and read earlier than the current “first-in first-out” standard of care. 
News | Breast Imaging | July 27, 2020
July 26, 2020 —  Zebra Medical Vision announced its sixth FDA 510