Case History: 65-year-old woman with right upper outer quadrant painless mass.
Case History: 65-year-old woman with right upper outer quadrant painless mass and complaints of multiple skin nodules and brown skin patches since childhood.
Study Assesses Ability of Mammography AI Algorithms to Predict Breast Cancer Risk
June 7th 2023Five artificial intelligence (AI) algorithms for mammography assessment were better at predicting breast cancer risk over five years than the Breast Cancer Surveillance Consortium (BCSC) risk model, according to new retrospective research involving over 13,000 women.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Large Study Shows Mixed Results in Comparison of DBT and Digital Mammography
May 31st 2023In a large retrospective study involving over 523,000 digital breast tomosynthesis (DBT) exams and over one million digital mammography (DM) exams, researchers found that DBT was associated with significantly lower recall rates but showed no advantage over DM in the diagnosis of interval or advanced breast cancer.
Multicenter Breast Ultrasound Study: AI Bolsters Accuracy and Specificity of BI-RADS Classifications
May 24th 2023Emerging breast ultrasound research showed the use of computer-aided diagnosis (CAD), powered by deep learning, led to 24 percent and 36.9 percent improvements in accuracy and specificity, respectively, in the use of BI-RADS classifications by radiologists without breast ultrasound expertise.
Digital Mammography Meta-Analysis Suggests AI Performs as Well as Radiologists
May 24th 2023Six reader studies on digital mammography revealed a pooled sensitivity rate of 80.8 percent for stand-alone artificial intelligence (AI) in comparison to 72.4 percent for radiologist assessment while seven historic cohort studies showed a 75.8 percent pooled sensitivity rate for stand-alone AI versus 72.6 percent for radiologist interpretation of digital mammography.
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