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ARRS: Dartmouth poster wins for CVD risk prediction potential using mammograms

AuntMinnie

A scientific poster on cardiovascular disease (CVD) risk prediction using fat-enlarged axillary nodes visualized on screening mammograms won the Summa Cum Laude Award at the 124th American Roentgen Ray Society (ARRS) annual meeting. This is defined by the American Heart Association as more than a 7.5%

Mammogram 278
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Late-stage breast cancer incidence increasing in U.S. women

AuntMinnie

The incidence of distant-stage breast cancer increased between 2004 and 2021 for the following: Asian women (APC, 2.90%; p White women meanwhile had an incident increase from 2004 to 2012 with an APC of 1.68%; (p = 0.01). However, the researchers did not observe such a trend from 2012 to 2021.

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Enlarged Lymph Nodes on Screening Mammograms Predict Cardiometabolic Disease, Cardiovascular Risk

Imaging Technology

milla1cf Fri, 05/10/2024 - 08:10 May 10, 2024 — According to the Summa Cum Laude Award-Winning Online Poster presented during the 124th ARRS Annual Meeting , fat-enlarged axillary nodes on screening mammograms can predict high cardiovascular disease (CVD) risk, Type 2 diabetes (T2DM), and hypertension (HTN). Rubino et al. and HTN (OR = 2.5,

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AI scoring predicts DCIS recurrence

AuntMinnie

The multicenter, retrospective study included data collected between 2012 and 2017 from 1,740 women with an average of 51.5 The researchers analyzed preoperative routine mammograms via a commercially available AI algorithm (Lunit Insight MMG, Lunit ). years who were treated for DCIS.

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Can customized reader pairing boost mammo double reading performance?

AuntMinnie

Gommers and colleagues sought to discover whether radiologist performance characteristics can be used to determine the best pairs of radiologists to double read screening mammograms. In total, the team included data from 3,592,414 mammography exams.

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CEM has tradeoffs for imaging extremely dense breasts

AuntMinnie

The researchers evaluated CEM’s diagnostic performance in this population, collecting data from consecutive CEM exams in asymptomatic women performed between 2012 and 2022. From the CEM exams, the team defined "low-energy" images as the equivalent of a 2D full-field digital mammogram.

Imaging 162
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Combining AI Models Improves Breast Cancer Risk Assessment

Imaging Technology

“In recent years, AI has been studied for the purpose of diagnosing breast cancer earlier by automatically detecting breast cancers in mammograms and measuring the risk of future breast cancer.” Diagnostic AI models are trained to detect suspicious lesions on mammograms and are well suited to estimate short-term breast cancer risk.