Articles tagged with Mammography
A retrospective cohort study found that AI algorithms can identify women at high risk of developing breast cancer 4-6 years before detection. The results suggest using commercial AI algorithms as a pathway to personalized screening approaches, leading to earlier cancer diagnosis.
A 10-year study found that digital breast tomosynthesis increases cancer detection rates and reduces advanced cancers compared to conventional 2D digital mammography. DBT detected more aggressive cancers at an earlier stage than digital mammography, leading to improved cancer detection and lower recall rates.
A new study found an association between breast arterial calcifications and cardiovascular disease in women, suggesting mammograms could be a warning sign. Women with breast arterial calcifications were more likely to experience atherosclerotic heart disease, with a 23% increased risk compared to those without.
A new study by UC Davis found that false-positive results can lead to women avoiding future screening, with Asian and Hispanic/Latinx women being the least likely to return. The research highlights the importance of continued screening for these groups despite a history of benign breast disease.
Women with regular annual mammograms had higher overall survival and lower late-stage cancer rates than those screened less frequently. The study's findings contradict current guidelines, suggesting a significant benefit to annual screening for women over 40.
A new study from the University of Copenhagen reveals that AI has helped detect significantly more cases of breast cancer and reduce radiologist workloads. The AI system has been used to analyze tens of thousands of X-rays every year, resulting in a 12% increase in detected cases, including more small tumours of one centimetre or less.
A study published in Radiology found that AI significantly improved breast cancer detection and reduced false-positive findings. The AI system detected more cancers while lowering the rate of unnecessary recalls, reducing radiologist workload by 33.4%.
A study of nearly 5,000 mammograms found that AI algorithms produced false positive results more frequently in Black and older patients, while Asian patients and younger patients were less likely to be flagged as suspicious. Healthcare institutions should consider the patient population they serve before purchasing an AI algorithm.
Text messaging and bulk ordering can significantly increase mammogram completion rates for breast cancer screening outreach. The study found that these interventions are effective in improving adherence to recommended screening guidelines.
The USPSTF updates its breast cancer screening recommendation to biennial mammography for women ages 40-74. However, the balance of benefits and harms is unclear for women over 75 or those with dense breasts. Breast cancer is a common cause of death among US women.
The US Preventive Services Task Force recommends biennial breast cancer screenings starting at age 40 for all women, aiming to enhance early detection and tackle disparities. However, challenges remain in ensuring equitable access to screening technologies and addressing gaps in evidence regarding AI-powered image interpretation.
A study published in the Canadian Association of Radiologists Journal shows an increase in breast cancer diagnoses among females in their Twenties, Thirties, and Forties. Younger women are more likely to be diagnosed at later stages and with more aggressive cancer.
Researchers developed an AI algorithm to identify normal mammograms and ran a simulation on patient data, revealing that AI can reduce unnecessary testing while maintaining cancer detection rates. The study suggests that AI can help doctors focus on more questionable scans, reducing false positives and improving workflows.
Researchers have developed a new AI model, AsymMirai, to predict 5-year breast cancer risk from mammograms. The model performs almost as well as the state-of-the-art Mirai, but with an interpretable reasoning process, making it a valuable adjunct to human radiologists.
A study from Florida Atlantic University identifies key social factors contributing to low breast cancer screening rates in the US. Socioeconomic status, access to healthcare, and insurance status are among the top influencers of breast cancer screening behavior.
The new AI model uses a visual map to explain each diagnosis, helping doctors follow its line of reasoning and check for accuracy. The tool aims to catch diseases in their earliest stages, making it easier on doctors and patients alike.
A new study published in Radiology found that annual breast cancer screening beginning at age 40 and continuing until 79 results in the highest mortality reduction. The study analyzed Cancer Intervention and Surveillance Modeling Network (CISNET) estimates and showed that annual screening reduces breast cancer deaths by 41.7%.
A novel imaging technique, low-dose positron emission mammography (PEM), shows promise in transforming breast cancer detection by providing high sensitivity and low false positive rates. PEM could potentially decrease healthcare costs and reduce unnecessary procedures for patients with dense breasts.
Researchers at the University of Münster found that DBT+SM detects invasive breast cancers more effectively than conventional DM, with a higher rate of early tumor stages. This approach may lead to improved screening effect and reduced breast cancer mortality.
Researchers found that patients who scheduled their own mammograms through an online patient portal had a 13 percentage point increase in screening completions. This simple intervention doubled the number of mammogram completions, resulting in approximately 4,500 more people getting screened.
Women who reduced mammography frequency three years after curative breast cancer surgery had comparable outcomes to those receiving annual screening. The Mammo-50 trial found that de-escalation did not worsen survival rates or increase recurrence, while also reducing healthcare burden and stress.
A study published in JAMA Oncology found that regular screening is not enough to prevent advanced breast cancer diagnoses, particularly among women of color who are overweight or obese. The researchers recommend primary prevention strategies to reduce the number of advanced cancer diagnoses.
A new AI-based risk model evaluates mammographic images to identify women at high risk of developing breast cancer. The study confirms that the method works well in different European populations, with 6.2% of women classified as high-risk having almost seven times the risk of developing breast cancer.
A study published at the Radiological Society of North America annual meeting found that attending regular mammograms can lower breast cancer mortality rates by up to 72%. Women who missed a scheduled mammogram had a significantly reduced survival rate, emphasizing the importance of adherence to screening schedules.
A deep learning AI model developed using mammographic images alone accurately predicted both ductal carcinoma in situ (DCIS) and invasive carcinoma, showing no bias across multiple races. The model outperformed traditional risk models in predicting breast cancer risk, providing a more accurate and equitable assessment.
Researchers found a significant long-term increased risk of breast cancer among women with false-positive mammography results. The risk is highest in women aged 60-75 and those with low breast density, facing a 60% increased risk over the subsequent 20 years.
A recent study published in JAMA Oncology found that false-positive mammography results may significantly increase the risk of developing breast cancer. The risk varies depending on individual characteristics and follow-up, highlighting the need for personalized screening strategies after a false-positive result.
A new poll found that 62% of people aged 50-80 disagree with using life expectancy as a factor in determining cancer screening guidelines. The majority also believes that some older adults should still receive cancer screenings even if guidelines don't recommend them.
A new clinical and research partnership has created an AI model that can predict whether cancerous tissue has been fully removed from the body during breast cancer surgery. The model performed as well as humans in identifying positive margins, especially in patients with higher breast density.
Researchers advocate for accessible breast cancer screening services for individuals with disabilities, highlighting physical barriers such as equipment accessibility and fragrance-free policies. They stress the need for social and procedural barriers removal through comprehensive training and resources.
Researchers found that an AI algorithm comparable to human readers demonstrated high sensitivity and specificity in detecting breast cancer. The study utilized a large dataset of mammographic exams and compared the performance of AI with human readers, finding no significant difference between the two.
Researchers at UMass Amherst are investigating the connection between PFAS chemicals and breast cancer. They will examine data from postmenopausal women to determine if PFAS concentrations are associated with less breast tissue involution, which may increase breast cancer risk.
A study published in Radiology found that combining short- and long-term breast cancer risk models using artificial intelligence can improve cancer risk assessment. The combined model showed an overall improved risk assessment for both interval and long-term cancer detection, identifying women at high risk for breast cancer.
A new study estimates that cancer screenings have saved the US at least $6.5 trillion and led to 12 million more years of life, with significant economic impact from breast, colon, cervical, and lung cancer screenings.
Researchers at Lund University found AI-supported mammography screening to be safe and effective, detecting 20% more cancers than standard double reading without increasing false positives. The study also reduced radiologists' screen-reading workload by 44%, saving approximately five months of time.
A randomized trial involving over 80,000 Swedish women found AI-supported mammography analysis to be as accurate as two breast radiologists working together in detecting breast cancer. The technology also reduced the screen-reading workload by nearly half without increasing false positives.
Researchers at MIT developed a wearable ultrasound device that can detect breast cancer in early stages, reducing the risk of late-stage diagnosis. The device, attached to a bra, allows users to image breast tissue from different angles and is portable, easy to use, and provides real-time monitoring.
A commercial AI tool did not provide additional benefit to mammography with supplementary ultrasound in patients with dense breasts. Mammography with supplementary ultrasound showed higher accuracy, specificity, and lower recall rate compared to AI alone or combined with US.
A recent study published in the journal CANCER found that supplemental ultrasound screening can detect breast cancers missed by mammography, but it requires careful targeting of high-risk groups. Women with dense breasts are at higher risk of interval invasive breast cancer, and clinicians should consider other breast cancer risk facto...
A recent study published in Radiology found that AI algorithms performed better than the standard Breast Cancer Surveillance Consortium (BCSC) risk model for predicting five-year breast cancer risk. The AI models extracted hundreds of additional mammographic features, leading to improved predictive performance.
Researchers analyzed BORIS mutations and protein expression in breast cancer tissue samples, finding frequent mutations associated with breast carcinoma progression. The study suggests the BORIS gene as a potential biomarker for breast cancer.
A study found that expert mammogram radiologists exhibit visual hindsight bias, leading to improved performance on blurry images after prior clear images were viewed. This bias can impact the detection of lesions and has implications for negligence lawsuits and patient outcomes.
A study of 5302 patients found no significant difference in bleeding events between those temporarily discontinuing and maintaining antithrombotic therapy during image-guided breast biopsies. The findings support the safety of continuing antithrombotic therapy, but counsel patients on bruising risk.
Researchers found that women with denser breasts who developed cancer had slower declining density over time. The study suggests using past history of density in addition to current estimates to better understand risk levels and potentially identify which breast is at risk.
The American College of Radiology (ACR) has introduced new guidelines recommending earlier and more intensive breast cancer screening for high-risk women, including Black women. According to the ACR, risk assessment by age 25 is necessary to determine if screening should start before age 40.
A study published in Radiology found that AI-based decision support systems can impair radiologist accuracy on mammograms, particularly for less experienced radiologists. Even highly experienced radiologists were adversely impacted by the system's judgments, highlighting the need for safeguards to mitigate automation bias.
Researchers discuss the importance of biomarkers in breast lump management, enabling patients to make informed decisions between proactive treatment and watchful waiting. The development of biomarkers could provide personalized risk assessments and guide treatment strategies for women with breast lumps.
A new study by Boston University School of Medicine found that non-Hispanic Black, Asian, and Hispanic women, as well as those with low literacy, experience increased anxiety and confusion when receiving breast density information. These groups are also less likely to feel informed about their breast cancer risk.
A study presented at the ARRS Annual Meeting reveals institutional variability in projection order and image acquisition timing for CEM protocol. Earlier-obtained recombined imaging is significantly preferred for cancer lesion characterization, with a trend towards prioritizing imaging the side of pathology.
Researchers found that patients with two or three separate areas of breast cancer in the same breast can opt for lumpectomies followed by whole-breast radiation therapy instead of mastectomies, resulting in better patient satisfaction and cosmetic outcomes. The study also showed that preoperative MRI scans reduced local recurrence rates.
A study published in Radiology found that digital breast tomosynthesis improved breast cancer screening performance and increased radiologists' interpretive accuracy compared to digital mammography. The results showed higher detection rates, sensitivity, and specificity for DBT, with 97.6% of assessed radiologists meeting recommended p...
A study found that high deductible health plans may lead women to skip additional testing after an abnormal mammogram finding, particularly those with lower incomes and education levels. This can result in delays in diagnosis and treatment, worsening breast cancer outcomes.
A printable multi-energy X-ray detector made from perovskite thin films has been developed with enhanced flexibility and sensitivity. The detector can operate in a broad energy range, from 0.1 KeV to tens of KeV, making it suitable for real-time detection and imaging applications such as disease diagnosis and explosives detection.
A study found that the change in guidelines led to a decline in mammography screening rates, particularly among women ages 50-74, an unintended consequence known as spillover. This effect resulted in 2.4 million fewer women being up-to-date with screening mammography between 2009 and 2018.
A large study of over 1 million women found DBT had a higher breast cancer detection rate and lower false positive rates compared to 2D digital mammography. The results suggest that DBT may be the more effective screening method for early breast cancer detection.
Axillary lymphadenopathy after a COVID-19 vaccine booster dose resolves at a mean of 102 days. Findings support follow-up intervals of at least 12 weeks to avoid delaying screening mammography for suspected vaccine-related lymphadenopathy.
Ultrafast MRI provides beneficial information for predicting DCIS upgrade to invasive cancer, including time to enhancement and lesion size. A study found that UF-MRI can accurately predict lesions diagnosed as DCIS at percutaneous biopsy that will be upgraded to invasive cancer at surgery.
Awareness of breast density increases perceived risk and uncertainties about screening choices in Spanish-speaking women. However, these effects are short-term and do not persist over time, suggesting that educational outreach may help clarify implications for personal risk and screening decisions.
A meta-analysis of 22 studies found that breast MRI was superior to other supplemental screening methods in detecting breast cancer in women with dense breasts. This is because breast MRI can detect even the smallest of cancers, making it a valuable tool for identifying high-risk patients.
A study of women ages 40 to 76 found that family history was the greatest perceived risk factor for breast cancer, with few considering breast density a risk. Comprehensive education is needed to address these misconceptions and reduce breast cancer risk.