New AI method enhances breast cancer risk prediction from mammograms

A new study from Washington University School of Medicine in St. Louis describes an innovative method of analyzing mammograms that significantly improves the accuracy of predicting the risk of breast cancer development over the following five years. Using up to three years of previous mammograms, the new method identified individuals at high risk of developing breast cancer 2.3 times more accurately than the standard method, which is based on questionnaires assessing clinical risk factors alone, such as age, race and family history of breast cancer.

The study is published Dec. 5 in JCO Clinical Cancer Informatics.

We are seeking ways to improve early detection, since that increases the chances of successful treatment. This improved prediction of risk also may help research surrounding prevention, so that we can find better ways for women who fall into the high-risk category to lower their five-year risk of developing breast cancer.”

Graham A. Colditz, MD, DrPH, senior author, associate director of Siteman Cancer Center, based at Barnes-Jewish Hospital and WashU Medicine, and the Niess-Gain Professor of Surgery

This risk-prediction method builds on past research led by Colditz and lead author Shu (Joy) Jiang, PhD, a statistician, data scientist and associate professor of surgery in the Division of Public Health Sciences at WashU Medicine. The researchers showed that prior mammograms hold a wealth of information on early signs of breast cancer development that can’t be perceived even by a well-trained human eye. This information includes subtle changes over time in breast density, which is a measure of the relative amounts of fibrous versus fatty tissue in the breasts.

For the new study, the team built an algorithm based on artificial intelligence that can discern subtle differences in mammograms and help identify those women at highest risk of developing a new breast tumor over a specific timeframe. In addition to breast density, their machine-learning tool considers changes in other patterns in the images, including in texture, calcification and asymmetry within the breasts.

“Our new method is able to detect subtle changes over time in repeated mammogram images that are not visible to the eye,” said Jiang, yet these changes hold rich information that can help identify high-risk individuals.

At the moment, risk-reduction options are limited and can include drugs such as tamoxifen that lower risk but may have unwanted side effects. Most of the time, women at high risk are offered more frequent screening or the option of adding another imaging method, such as an MRI, to try to identify cancer as early as possible.

“Today, we don’t have a way to know who is likely to develop breast cancer in the future based on their mammogram images,” said co-author Debbie L. Bennett, MD, an associate professor of radiology and chief of breast imaging for the Mallinckrodt Institute of Radiology at WashU Medicine. “What’s so exciting about this research is that it indicates that it is possible to glean this information from current and prior mammograms using this algorithm. The prediction is never going to be perfect, but this study suggests the new algorithm is much better than our current methods.”

AI improves prediction of breast cancer development

The researchers trained their machine-learning algorithm on the mammograms of more than 10,000 women who received breast cancer screenings through Siteman Cancer Center from 2008 – 2012. These individuals were followed through 2020, and in that time 478 were diagnosed with breast cancer.

The researchers then applied their method to predict breast cancer risk in a separate set of patients -; more than 18,000 women who received mammograms through Emory University in the Atlanta area from 2013 – 2020. Subsequently, 332 women were diagnosed with breast cancer during the follow-up period, which ended in 2020.

According to the new prediction model, women in the high-risk group were 21 times more likely to be diagnosed with breast cancer over the following five years than were those in the lowest-risk group. In the high-risk group, 53 out of every 1,000 women screened developed breast cancer over the next five years. In contrast, in the low-risk group, 2.6 women per 1,000 screened developed breast cancer over the following five years. Under the old questionnaire-based methods, only 23 women per 1,000 screened were correctly classified in the high-risk group, providing evidence that the old method, in this case, missed 30 breast cancer cases that the new method found.

The mammograms were conducted at academic medical centers and community clinics, demonstrating that the accuracy of the method holds up in diverse settings. Importantly, the algorithm was built with robust representation of Black women, who are usually underrepresented in development of breast cancer risk models. The accuracy for predicting risk held up across racial groups. Of the women screened through Siteman, most were white, and 27% were Black. Of those screened through Emory, 42% were Black.

In ongoing work, the researchers are testing the algorithm in women of diverse racial and ethnic backgrounds, including those of Asian, southeast Asian and Native American descent, to help ensure that the method is equally accurate for everyone.

The researchers are working with WashU’s Office of Technology Management toward patents and licensing on the new method with the goal of making it broadly available anywhere screening mammograms are provided. Colditz and Jiang also are working toward founding a start-up company around this technology.

Jiang S, Bennett DL, Rosner BA, Tamimi RM, Colditz GA. Development and validation of a dynamic 5-year breast cancer risk model using repeated mammograms. JCO Clinical Cancer Informatics. Dec. 5, 2024.

This work was supported by Washington University School of Medicine in St. Louis.

Jiang and Colditz have patents pending related to this work, predicting disease risk using radiomic images.