Articles tagged with Mammography
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
Researchers used AI to analyze mammograms and found that women who developed breast cancer had increasing risk scores over time, while those who did not had stable scores. The study suggests that image-based AI risk scores can predict future breast cancer risk in women without a known genetic mutation or family history.
A new study from Kaiser Permanente found that combining AI mammographic risk scores with polygenic and clinical risk scores more accurately identifies women at high risk of developing breast cancer than clinical risk scores used alone. The combined model was found to improve prediction accuracy, particularly among women at highest risk.
A study published in Radiology found that three commercially available AI-based computer-assisted detection (AI-CAD) systems can identify early mammographic signs of breast cancer up to 6 years before diagnosis. This could help radiologists spot potential future cancers and allow for earlier intervention.
Researchers used an open-source AI model called Mirai to quickly identify high-risk patients and expedite their diagnostic process. This led to a significant reduction in wait times, with some women receiving biopsy results within days of having their mammograms done.
A new survey found that nearly half of women incorrectly believe annual mammograms should start at age 50. Experts recommend yearly mammograms starting at age 40 for average-risk women. This delay can lead to cancers being found at a later stage, when treatment can be more complex.
Researchers have reported early, reassuring results from the LORD trial on active surveillance for low-risk DCIS. The study found that patients with active surveillance did not experience worse early outcomes compared to those who underwent immediate surgery, and that many chose active surveillance over surgery.
Researchers compare prostate cancer screening to breast cancer screening, finding similarities in identifying significant cancers and reducing mortality. The study suggests that prostate cancer screening should be reconsidered, offering benefits over harms, and highlights the need for further analysis on costs.
A study published in the European Heart Journal found that AI analysis of mammograms can predict the risk of serious heart disease in women. Women with mild calcification were 30% more likely to suffer serious cardiovascular disease, while those with severe calcification were two to three times more likely.
The Alliance for Clinical Trials in Oncology will host a webinar highlighting recent clinical advances in breast cancer, multiple myeloma, and leukemia. Researchers will present key findings from ASH and SABCS meetings, impacting treatment outcomes.
Researchers at Mass General Brigham have demonstrated the technical feasibility of using ultra-low field magnetic resonance imaging (MRI) for breast imaging, offering an alternative to existing methods and potentially reducing barriers to screening. Further study is needed to establish diagnostic performance and refine technology.
Women with stage IV breast cancer detected through screening have a 60% chance of survival ten years after diagnosis, compared to under 20% without screening. Screening may not be too late to treat successfully, even at the most serious stage of the disease.
The study found a 12% reduction in interval cancers diagnosed after screening, as well as fewer aggressive sub-type cancers detected. AI-supported mammography also increased cancer detection without an increase in false positives.
A new study found that calcium accumulation in breast arteries and its progression on mammograms can predict future cardiovascular disease. Women with more severe calcification and those whose calcification worsened over time had a higher risk of heart-related events, such as heart attack, stroke, and death.
A new study found that breast density notifications can lead to increased levels of confusion and anxiousness among women, contradicting the intended goal of empowering them. Women with denser breast tissue may require additional screening methods, which can result in higher out-of-pocket costs.
A new study published in the Journal of the American College of Radiology found that targeted outreach can increase lung cancer screenings for eligible women who undergo mammograms. The CALM model, which coordinates lung screening with mammography, exceeded its target enrollment and demonstrated effectiveness in increasing uptake. By i...
Researchers found that women notified of their dense breasts felt more anxious and confused about their breast health, with increased intentions to talk to their GP. The study suggests that population breast density notification should be carefully considered due to potential adverse outcomes.
A study of seven outpatient facilities found that 20-24% of all breast cancers diagnosed between 2014 and 2024 were in women aged 18-49. Invasive cancer cases accounted for 80.7%, often with aggressive types, challenging age-based screening cutoffs.
A new AI image-only model has been shown to provide stronger and more precise risk stratification for five-year breast cancer prediction than traditional breast density assessment. The model was trained on 421,499 mammograms and showed a fourfold higher cancer incidence in high-risk women compared to those with average risk.
The new system can reveal early cancers, lung disease, hidden material defects and changes in porosity without multiple exposures or complex mechanical movement. This method produces low-dose and faster images, lowering patients' radiation dose and making clinical translation feasible.
Researchers developed an AI tool to identify women at higher risk of developing interval breast cancers, which have a worse prognosis. The tool predicted 42.4% of interval cancers for women with the top 20% scores, suggesting it could optimize breast cancer screening programs by identifying those who need supplemental imaging.
Women who don't attend their first mammogram screening face a 40% higher long-term risk of dying from breast cancer. Targeted interventions are needed to boost adherence and reduce mortality risk among initial non-attenders.
A new study from Karolinska Institutet reveals that women who skip their first mammogram are more likely to be diagnosed with advanced breast cancer and die from the disease. The study found a 32% decline rate among women who missed their first screening, leading to poorer prognosis and higher mortality rates.
The PRISM trial will examine the impact of AI on mammogram accuracy and patient experience, with hundreds of thousands of mammograms analyzed. The study aims to understand whether AI enhances cancer detection by radiologists or leads to more false alarms.
A new study found that combining molecular breast imaging (MBI) and digital breast tomosynthesis (DBT) increases invasive cancer detection while moderately increasing recall rates. MBI detected an additional 6.7 cancers per 1,000 screenings in women with dense breasts.
A new study from the University of California - Los Angeles Health Sciences suggests that regular mammograms may still offer significant benefits for women in their 80s. The study found that screened women were more likely to have surgery to remove the tumor and had a lower risk of cancer returning and dying compared to unscreened women.
A Lund University AI model analyzed mammograms to identify breast cancer patients at high risk of armpit metastasis, suggesting that 41.7% of axillary surgeries could be avoided. The study's findings have the potential to revolutionize breast cancer treatment by enabling more personalized care and reducing unnecessary surgeries.
A new AI algorithm using routine mammogram images and age can predict major cardiovascular disease risk as accurately as standard methods. The study suggests that mammography may offer a cost-effective 'two for one' effective screening option for women, leveraging existing health infrastructure.
A new machine learning model predicts heart disease risk in women by analyzing mammograms, offering a 'two-for-one' screening approach that combines breast and cardiovascular screenings. The model performs comparable accuracy to traditional risk calculators without requiring extensive clinical data.
An AI algorithm based on routine mammogram images plus age can predict a woman's risk of major cardiovascular disease with high accuracy. The algorithm leverages existing health infrastructure and offers a cost-effective 'two for one' screening option.
A new AI hybrid reading strategy for screening mammography reduces radiologist workload by 38% while maintaining comparable cancer detection and recall rates. The strategy incorporates uncertainty quantification, allowing AI to provide certainty estimates for predictions.
Researchers at Radboud University Medical Center found that AI detects breast cancer on mammograms more frequently than radiologists. The study analyzed 42,000 breast scans and showed that one radiologist working with AI detects more tumors than two radiologists alone.
Researchers evaluated over 1,500 AI algorithms for detecting breast cancers on mammography images, achieving median rates of 98.7% specificity and 27.6% sensitivity. Combining top-performing algorithms resulted in sensitivity boosts to 60.7% and 67.8%, comparable to average screening radiologists.
A new AI-based technology analyzes mammograms to predict a woman's five-year breast cancer risk, outperforming standard methods by 2.2 times. The system provides an absolute risk score comparable to national incidence rates, enabling targeted prevention and early detection.
A new AI algorithm has been shown to detect nearly one-third of interval breast cancers missed at screening using digital breast tomosynthesis (DBT), according to a study published in Radiology. The algorithm improved the performance of DBT, reducing interval cancers.
An AI model accurately detected tumor locations and outperformed benchmark models in detecting breast cancer, according to a study published in Radiology. The model learned a robust representation of benign cases to better identify abnormal malignancies, even with underrepresented data.
A new breast scan system combines photoacoustic and ultrasound imaging to produce clear 3D images of common breast cancer subtypes. The system, called OneTouch-PAT, takes less than a minute to complete and excels at detecting suspicious lesions in women with dense breast tissue.
A recent survey by the Annenberg Public Policy Center found that nearly half of Americans believe women should begin regular mammograms at age 40, while younger women are less certain. Women aged 18-29 were most likely to choose an incorrect age, with 27% selecting 30 years old as the start age.
A new AI-assisted technique called PACT has been developed to help differentiate between suspicious and healthy tissue in breast imaging. The method, which combines light and sound into a single modality, performs as well as or better than conventional techniques like mammography and MRI.
Eliminating the fee increased overall usage by 7.8 percentage points, particularly benefiting underserved populations, including non-English speakers and those from socioeconomically disadvantaged areas. Despite gains, some disparities persisted, highlighting the need for additional strategies to ensure equitable access.
Patients with breast cancer detected through routine screening mammography have improved clinical outcomes, including lower odds of advanced stage breast cancer and death. In contrast, symptom-detected cases were more frequent in older women and had higher mortality rates.
Emerging imaging technologies like Tomosynthesis, Contrast-Enhanced Spectral Mammography, and Molecular Breast Imaging improve detection of high-risk populations and dense breast tissue. AI Integration, Noninvasive Biomarkers, and Hybrid Techniques hold promise for reducing radiation risks and overdiagnosis.
A new study identified six distinct breast tissue patterns associated with an increased risk of invasive breast cancer, particularly among Black women. The radiomic phenotypes may help predict the likelihood of false-negative mammograms or interval cancer diagnoses.
A survey of patients who underwent breast cancer screening mammograms found most support AI's role as a second reader, but concerns about data privacy and bias persisted. Demographic factors such as education and racial background played a significant role in shaping patient perceptions of AI.
A cohort study found that women with screen-detected estrogen receptor–positive or human epidermal growth factor receptor 2–negative breast cancer who underwent prior screening had an earlier stage of diagnosis and lower breast cancer mortality compared to those who did not undergo prior screening.
A new study found that interval cancers, which are detected between two screening sessions, account for 30% of all breast cancers. Women with high breast density, hormone therapy, and a family history of breast cancer are at increased risk. More frequent or improved screening methods could significantly improve early detection rates.
A deep learning model analyzes breast arterial calcification and translates results into a cardiovascular risk score, helping identify patients at higher risk of heart disease. The tool is particularly useful for younger women, who can benefit from early interventions.
Breast cancer mortality rates have stopped decreasing in two age groups: women under 40 and those over 74. This shift is attributed to an increase in stage IV breast cancers at diagnosis, leading to poor prognoses.
A new federal mandate aims to improve patient awareness about breast density, which affects cancer detection and risk. Women with dense breasts may need supplemental screening, such as MRI, to detect breast cancer earlier, but this comes with potential downsides.
A study found racial and ethnic minorities are less likely to receive standard-of-care diagnostic imaging after abnormal screening mammograms compared to white patients. Minority groups were also less likely to have access to same-day biopsy services, despite similar availability of most diagnostic services.
A study found significant racial disparities in who received same-day diagnostic services and biopsies after abnormal mammogram findings, with Black women being less likely to receive same-day biopsies. Rural-resident patients were more likely to receive same-day diagnostic services than urban residents.
A new study published in The Lancet Digital Health found that AI-supported breast cancer screening increased the detection of early-stage invasive cancers by 24% compared to traditional screening. AI also identified pre-cancerous lesions with a 51% higher rate, without increasing false positives.
The Tomosynthesis Mammographic Imaging Screening Trial (TMIST) has successfully enrolled over 108,000 women worldwide for a randomized controlled trial comparing 2D and 3D mammography. The study aims to determine whether 3D mammography is more effective in reducing advanced breast cancer risk.
A recent study published in Nature Medicine found that AI significantly improves breast cancer detection rates, detecting an additional cancer case per 1,000 women screened. The results show improved efficiency without increasing false positives or unnecessary follow-ups.
A cohort study found a decrease in localized invasive breast cancer and regional invasive breast cancer since the 2009 guideline changes, but no decrease in advanced cancer stages. Further research is needed to understand these trends.
A recent study found that nearly two-fifths of women aged 40-49 did not receive biennial mammography screening, highlighting significant gaps in early detection. To optimize breast cancer detection, ensuring equitable adherence to U.S. Preventive Services Task Force recommendations is crucial.
Artificial intelligence is enhancing breast cancer risk prediction through mammographic analysis, uncovering key features that predict breast cancer risk. These AI-generated features may inform personalized screening strategies, such as more frequent or less frequent screenings for women at high or low risk of breast cancer.
A new study improves breast cancer risk prediction by analyzing up to three years of previous mammograms, identifying individuals at high risk 2.3 times more accurately than the standard method. The AI method considers subtle changes in mammogram images and holds up well across diverse settings.
A self-pay, AI-enhanced breast cancer screening program was offered to patients across 10 clinical practices, resulting in a 43% higher cancer detection rate among enrolled women. The AI-powered program attributed 21% of the increase in cancer detection, with higher-risk patients choosing to enroll more frequently.
Researchers developed a new photoacoustic imaging technique that addresses skin tone bias in breast cancer detection. The technique, combined with specific wavelengths and beamforming methods, enhances target visibility across all skin tones, providing clearer images with improved signal-to-noise ratios.