Using advanced medical algorithms, researchers have identified six breast texture patterns that may indicate a high risk for breast cancer. In a new study, one of the largest of its kind, published in the journal Radiology, a research team analyzed the mammograms of over 30,000 women and condensed the information down into six phenotypes. With this new information, the research team hopes to improve breast cancer screening and prevention. Breast Tissue PatternsFull-field digital mammography (FFDM) images in a 52-year-old woman show a high-risk phenotype (top), and FFDM images in a 58-year-old woman show a low-risk phenotype (bottom). The high-risk phenotype was defined by having high values of the first principal component (PC1) and was assigned to cluster 3 (top row), and the low-risk phenotype was defined by having low values of the PC1 and was assigned to cluster 1 (bottom row). The index images (left side; before any diagnosis of cancer) are provided as well as follow-up images obtained at either the time of cancer diagnosis (for the high-risk phenotype, top right) or at the last follow-up (low-risk phenotype, bottom right). The red circle indicates the location of subsequent breast cancer. Both women had a Breast Imaging Reporting and Data System breast density of C. The woman at high risk for breast cancer was Black and the woman at low risk for breast cancer was White. (Image Credit: Radiological Society of North America (RSNA))For those with denser breast tissue, meaning the breast contains more glandular tissue instead of fatty tissue, detecting cancer with a mammogram can be more difficult. This is because dense tissue and cancer growth can look similar in the mammogram image, as each appears white. Those with dense breast tissue also have a higher risk of developing breast cancer. To make matters more difficult, women with similar dense breast tissue could actually have different tissue patterns. However, understanding these breast tissue patterns and characteristics could lead to improved monitoring and discovery of breast cancer. “We hypothesized that some patterns or phenotypes would be associated with a high risk of future breast cancer and suggest which women may benefit from supplemental screening or prevention strategies,” said Celine M. Vachon, Ph.D., a professor of epidemiology at the Mayo Clinic in Rochester, Minnesota and one of the lead study authors in a press release. “Other phenotypes could be associated with low risk, ultimately, suggesting less frequent screening.”Breast Cancer Algorithms and Medical Images For this study, the research team used radiomics — a method that uses data algorithms to extract information from a medical image — on mammograms from over 30,000 patients from three different cohorts. The patients didn’t have a prior history of breast cancer. The radiomics pulled patterns and characteristics in the breast tissue that weren't often visible to the human eye. From the information, the radiomics pulled 390 features and separated them into six different phenotypes. The research team then applied these phenotypes to 3,500 patients, some of whom had developed breast cancer and some of whom had not. The results found that these phenotypes were associated with a higher risk of breast cancer in both Black and white patients. “We were surprised to find that these radiomic phenotypes showed suggestion of a stronger risk among Black vs. white women,” said Despina Kontos, Ph.D., Herbert and Florence Irving Professor of Radiological Sciences and chief research information officer at Columbia University Irving Medical Center, and study co-author, in a press release. “This is particularly important as breast cancer tends to be more aggressive in Black women, highlighting the need for novel risk factors in this population.”Making Breast Cancer Detection More Accurate According to the study, these phenotypes could cause a patient’s mammogram to come back as a false negative, meaning the cancer diagnosis was missed. “Understanding who is at greatest risk of invasive breast cancer, especially the most aggressive types, is crucial for preventing cancer and diagnosing it early for potentially the choice of less intensive treatments,” said co-senior author Karla M. Kerlikowske, M.D., professor of medicine and epidemiology and biostatistics at University of California San Francisco in a press release. The team plans to implement these findings into other applications that could help more accurately detect breast cancer and decipher who is more at risk. “Our next steps include extending our investigations to larger groups of women in the U.S. population, especially examining 3D mammograms, and combining these radiomic risk factors with genetic and other lifestyle factors to improve our ability to define who is (and who is not) at increased risk of invasive breast cancer,” Vachon said in a press release.Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:A graduate of UW-Whitewater, Monica Cull wrote for several organizations, including one that focused on bees and the natural world, before coming to Discover Magazine. Her current work also appears on her travel blog and Common State Magazine. Her love of science came from watching PBS shows as a kid with her mom and spending too much time binging Doctor Who. Source: https://www.discovermagazine.com/health/patterns-in-breast-tissue-could-reveal-risk-of-invasive-breast-cancer #patterns #breast #tissue #could #reveal #risk #invasive #cancer