A major new study from the University of California – San Francisco (UCSF) has found a smarter way to screen for breast cancer. This research moves away from standard age-based mammogram rules. Instead, it tailors screening schedules to each woman's personal risk factors. This approach aims to find more cancers while reducing unnecessary tests for many women. The findings come from the first phase of the WISDOM study, which included 46,000 women across the United States. ^[1][2]### How Personalized Screening Works The new screening method starts with a thorough personal risk assessment. This involves looking at several key factors. Doctors consider genetic test results, including but not limited to BRCA1 and BRCA2 genes. They also review a woman's family history, even beyond immediate relatives. Health habits, weight, reproductive history, and breast tissue density all play a role. A woman's past medical history, such as previous biopsies, also helps create a full picture. ^[1]With all this information, women are placed into different risk groups. Those at higher risk might start mammograms earlier and have them more often. They may also receive other imaging tests, like MRIs. For women identified as lower risk, the screening plan recommends fewer mammograms. This means less radiation exposure and less worry from false alarms. T^[1]he UCSF team found this tailored screening could still catch advanced cancers in time. It did so without putting low-risk women through unneeded tests. ^[1]### AI Enhances Detection and Efficiency Artificial intelligence (AI) is also changing breast cancer screening. AI tools work alongside radiologists to analyze mammograms. These systems are trained on vast numbers of images to spot subtle signs of cancer that human eyes might miss. T^[3]his technology can flag suspicious areas or help assess a patient's risk. ^[3][4]Studies show AI significantly boosts cancer detection rates. A German study involving 461,818 women found AI-supported screening increased detection by 17.6% compared to standard methods. S^[4][5]eparately, a Swedish trial with over 80,000 women reported AI detected 20% more cancers. E^[3][6]ven higher, Lund University's MASAI trial in Sweden found a 29% increase in cancer detection with AI support. A^[7]nother study in Hungary showed an AI tool called Mia detected up to 13% more breast cancers. ^[8]AI also helps reduce false positives, which are mistaken cancer alarms. One study saw almost a 6% reduction in false positives in the U.S. and 1.2% in the U.K. when AI was used. T^[3]he German study also found a higher detection rate without increasing false positive recalls. A^[4][5] Danish study reported a 20.5% decrease in recall rates, meaning fewer women were called back for unnecessary follow-up tests. B^[9]eyond accuracy, AI can also ease the workload for radiologists. The Swedish trial showed doctors spent 44% less time reading mammograms with AI support. A^[6] Danish study noted a 33.5% reduction in radiologist reading workload. ^[9]### The WISDOM Study's Impact The WISDOM study's initial phase involved 46,000 women from across the United States. R^[2]esearchers used detailed risk assessments instead of a fixed screening schedule for everyone. They looked at genes, family history, lifestyle habits, and breast density. T^[1]his is different from the old method, which assumes all women face the same risk level at the same age. T^[1]he UCSF team confirmed that this personalized screening successfully detected advanced cancers. It did so without over-testing women at low risk. ^[1]Most women in the study's observational group preferred the personalized model, with 89% choosing it. D^[2]r. Laura J. Esserman, director of the UCSF Breast Care Center and the study's first author, believes these findings should change clinical guidelines. "These findings should transform clinical guidelines for breast cancer screening and alter clinical practice," Dr. Esserman said. S^[2]he added, "The personalized approach begins with risk assessment, incorporating genetic, biological, and lifestyle factors, which can then guide effective prevention strategies." T^[2]he research was published on December 12 in JAMA and presented at the San Antonio Breast Cancer Symposium. ^[2]### Addressing Challenges and Future Steps While these new methods offer great promise, challenges remain. Some concerns exist about potential bias in AI systems. A 2024 study found that AI was more likely to incorrectly flag mammograms as suspicious for Black women and older women. Black women were 50% more likely than white women to have a false positive. Older women (ages 71-80) were 90% more likely than younger women (ages 51-60) to have a false positive. R^[3]esearchers are working to address these biases to ensure equitable care. The WISDOM study is continuing to expand its reach. The expanded study, known as WISDOM 2.0, aims to include women as young as 30. It plans to enroll thousands more women who may have a higher risk of aggressive cancers. The goal is to reach 72,000 participants. T^[10]his effort seeks to determine who benefits most from more screening and who can safely have fewer, leading to better breast cancer outcomes. ^[10]Breast cancer remains a significant global health issue. In 2022, there were an estimated 2.3 million new cases and 670,000 deaths worldwide. S^[1]marter screening methods, like personalized risk assessment combined with advanced AI tools, offer hope for earlier detection, reduced unnecessary procedures, and ultimately, saving more lives. These innovations are paving the way for a new era in breast cancer care.

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