Purpose: To compare screening recall rates and cancer detection rates of tomosynthesis plus conventional digital mammography to those of conventional digital mammography alone.
Materials And Methods: All patients presenting for screening mammography between October 1, 2011, and September 30, 2012, at four clinical sites were reviewed in this HIPAA-compliant retrospective study, for which the institutional review board granted approval and waived the requirement for informed consent. Patients at sites with digital tomosynthesis were offered screening with digital mammography plus tomosynthesis. Patients at sites without tomosynthesis underwent conventional digital mammography. Recall rates were calculated and stratified according to breast density and patient age. Cancer detection rates were calculated and stratified according to the presence of a risk factor for breast cancer. The Fisher exact test was used to compare the two groups. Multivariate logistic regression was used to assess the effect of screening method, breast density, patient age, and cancer risk on the odds of recall from screening.
Results: A total of 13 158 patients presented for screening mammography; 6100 received tomosynthesis. The overall recall rate was 8.4% for patients in the tomosynthesis group and 12.0% for those in the conventional mammography group (P < .01). The addition of tomosynthesis reduced recall rates for all breast density and patient age groups, with significant differences (P < .05) found for scattered fibroglandular, heterogeneously dense, and extremely dense breasts and for patients younger than 40 years, those aged 40-49 years, those aged 50-59 years, and those aged 60-69 years. These findings persisted when multivariate logistic regression was used to control for differences in age, breast density, and elevated risk of breast cancer. The cancer detection rate was 5.7 per 1000 in patients receiving tomosynthesis versus 5.2 per 1000 in patients receiving conventional mammography alone (P = .70).
Conclusion: Patients undergoing tomosynthesis plus digital mammography had significantly lower screening recall rates. The greatest reductions were for those younger than 50 years and those with dense breasts. A nonsignificant 9.5% increase in cancer detection was observed in the tomosynthesis group.
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http://dx.doi.org/10.1148/radiol.13130307 | DOI Listing |
Sci Rep
December 2024
Department of Health Disparities Research, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Black women (BW) experience age-adjusted breast cancer mortality rates that are 40% higher than White women. Although, screening rates for breast cancer are similar between White and Black women, differences in mammography utilization exist among women with lower socioeconomic status (SES). Moreover, perceived everyday discrimination (PED) has been shown to have an inverse relationship on health screening behavior among BW.
View Article and Find Full Text PDFSci Rep
December 2024
Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Department of Surgery, University of Vermont, Burlington.
Importance: The 2009 US Preventive Services Task Force breast cancer screening guideline changes led to decreases in screening mammography, raising concern about potential increases in late-stage disease and more invasive surgical treatments.
Objective: To investigate the incidence of breast cancer by stage at diagnosis and surgical treatment before and after the 2009 guideline changes.
Design, Setting, And Participants: This population-based, epidemiologic cohort study of women aged 40 years or older used 2004 to 2019 data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program.
Tomography
December 2024
Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
View Article and Find Full Text PDFJ Imaging
December 2024
Computer Science and Engineering Department, College of Engineering, University of Nevada, Reno, Main Campus, Reno, NV 89557, USA.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches.
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