Today's Date: March 2, 2024
University of Phoenix Continues Nationwide Networking Series, "Small Bites, Big Connections," Fostering Alumni Community Bonds   •   Canada is connecting more than 150 Northwest Territories households to high-speed Internet and bringing mobile connectivity to r   •   Minister Qualtrough announces investments in sport and physical activity opportunities for Indigenous communities   •   Ventas to Participate in Investor Meetings at Citi 2024 Global Property CEO Conference   •   /C O R R E C T I O N -- Lexus/   •   Robust AI: Transforming Education and Healthcare with AI-Powered Teacher Apps   •   Standing Buffalo and Canada sign roadmap to advance reconciliation and rebuild their relationship   •   Barnes & Noble Education Receives Continued Listing Standard Notice From NYSE   •   WM Elects Tom Bené to Board of Directors   •   EMPOWER Leads Foster Care Services for All Children and Families in the Metroplex East Region   •   Strong Tower Consulting Named One of the Best Places to Work in Consulting   •   American Cancer Society Receives NBA Foundation Grant to Increase Underrepresented Students Entering Cancer Research Careers   •   Calvin Klein Fragrances announces Idris and Sabrina Elba as the face of new Calvin Klein ETERNITY AROMATIC ESSENCE fragrance cam   •   Pittsburgh 12th Grader Selected to Join Million Girls Moonshot Flight Crew   •   Duncan's First Nation celebrates the opening of a new water treatment plant   •   AHF Opens Food Pantry for San Diego Veterans   •   Croptimistic Releases Inaugural Sustainability Report   •   1,000 Dream Fund Kicks Off Women's History Month Celebration with Twitch Charity Fundraiser in Partnership with Twitch Women's U   •   Children’s Hospital Los Angeles Launches Ninth Annual Make March Matter® Fundraising Campaign   •   The Family Initiative (TFI) Becomes One of the Largest Community-Based Care Child Welfare Agencies in the U.S.
Bookmark and Share

AI Model Predicts Breast Cancer Risk Without Racial Bias

AI Model Predicts Breast Cancer Risk Without Racial Bias

PR Newswire

CHICAGO, Nov. 29, 2023 /PRNewswire/ -- A deep learning artificial intelligence (AI) model that was developed using only mammogram image biomarkers accurately predicted both ductal carcinoma in situ (DCIS) and invasive carcinoma, according to research being presented today at the annual meeting of the Radiological Society of North America (RSNA). Additionally, the model showed no bias across multiple races.

Traditional breast cancer risk assessment models use information obtained from patient questionnaires, such as medical and reproductive history, to calculate a patient's future risk of developing breast cancer.

"In the domain of precision medicine, risk-based screening has been elusive because we have not been able to accurately evaluate a woman's risk of developing breast cancer," said study lead author Leslie R. Lamb, M.D., M.Sc., a breast radiologist at Massachusetts General Hospital (MGH) in Boston. "Even the best existing traditional risk models do not perform well on the individual level."

Traditional risk models have also demonstrated poor performance across different patient races, most likely due to the data used to develop the model.

"Traditional models likely have racial biases due to the populations on which they were developed," Dr. Lamb said. "Several of the commonly used models were developed on predominantly European Caucasian populations."

According to the American Cancer Society, Black women demonstrate the lowest 5-year relative survival rate for breast cancer among all racial and ethnic groups. This translates to a persistent 6% to 8% disparity in 5-year survival rates between Black and white women across all breast cancer types.

To accurately determine breast cancer risk, foster early detection and improve patient survival rates, it is important that risk models are developed that are applicable across different populations.

A deep learning AI risk assessment model developed using mammographic images alone can outperform traditional risk assessment models in future breast cancer development while also mitigating the racial biases seen in traditional models.

In the first study of its kind, Dr. Lamb and colleagues sought to assess the performance of an image-based deep learning risk assessment model in predicting both future invasive breast cancer and DCIS across multiple races.

The model's performance was assessed by comparing areas under the receiver operating characteristic curve (AUC) with the DeLong test. The AUC score measures the predictive rate of the model on a scale of from 0 to 1. Multiple prior studies have estimated traditional risk model performance measured by AUC in the range of 0.59-0.62 for white women, with much lower performance in women of other races.

The multisite study included 129,340 routine bilateral screening mammograms performed in 71,479 women between 2009 to 2018 with five-year follow-up data. Patient demographics were obtained from electronic medical records, and instances of cancer were identified from the regional tumor registry.

The racial makeup of the study group included white (106,839 exams), Black (6,154 exams), Asian (6,435 exams), self-reported other races (6,257 exams) and unknown (3,655 exams). The mean age of the women was 59 years old. 

The deep learning model consistently outperformed traditional risk models in predicting a woman's risk of developing DCIS, which is early-stage breast cancer, and invasive breast cancer, which is cancer that has potential to spread.

"The model is able to translate the full diversity of subtle imaging biomarkers in the mammogram, beyond what the naked eye can see, that can predict a woman's future risk of both DCIS and invasive breast cancer," Dr. Lamb said. "The deep learning image-only risk model can provide increased access to more accurate, equitable and less costly risk assessment."

The predictive rate of both DCIS and invasive cancer was 0.71 across all races. The AUC in predicting DCIS was 0.77 in non-white patients and 0.71 in white patients. The AUC in predicting invasive cancer was 0.72 in non-white patients and 0.71 in white patients.

"This is a particularly exciting domain for AI, as it demonstrates the opportunity to apply 'AI for good'—to reduce well-known racial disparities in risk assessment," said senior author Constance D. Lehman, M.D., Ph.D., a breast radiologist at MGH. "We are now poised to translate these findings into improved clinical care for our patients."

Additional co-authors are Sarah F. Mercaldo, Ph.D., and Andrew R. Carney, M.S.

Note: Copies of RSNA 2023 news releases and electronic images will be available online at RSNA.org/press23.

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois. (RSNA.org)

Editor's note: The data in these releases may differ from those in the published abstract and those actually presented at the meeting, as researchers continue to update their data right up until the meeting. To ensure you are using the most up-to-date information, please call the RSNA Newsroom at 1-312-791-6610.

For patient-friendly information on breast imaging, visit RadiologyInfo.org.

Cision View original content to download multimedia:https://www.prnewswire.com/news-releases/ai-model-predicts-breast-cancer-risk-without-racial-bias-301997405.html

SOURCE Radiological Society of North America (RSNA)



Back to top
| Back to home page
Video

White House Live Stream
LIVE VIDEO EVERY SATURDAY
Breaking News
alsharpton Rev. Al Sharpton
9 to 11 am EST
jjackson Rev. Jesse Jackson
10 to noon CST


Video

LIVE BROADCASTS
Sounds Make the News ®
WAOK-Urban
Atlanta - WAOK-Urban
KPFA-Progressive
Berkley / San Francisco - KPFA-Progressive
WVON-Urban
Chicago - WVON-Urban
KJLH - Urban
Los Angeles - KJLH - Urban
WKDM-Mandarin Chinese
New York - WKDM-Mandarin Chinese
WADO-Spanish
New York - WADO-Spanish
WBAI - Progressive
New York - WBAI - Progressive
WOL-Urban
Washington - WOL-Urban

Listen to United Natiosns News