Introduction: While digital breast tomosynthesis (DBT) has proven to enhance cancer detection and reduce recall rates (RR), its integration into BreastScreen Australia for screening has been limited, in part due to perceived cost implications. This study aims to assess the cost effectiveness of digital mammography (DM) compared with synthesized mammography and DBT (SM + DBT) in a first round screening context for short-term outcomes.
Methods: Clients recalled for nonspecific density (NSD) as a single lesion by both readers at the Northern Sydney Central Coast BreastScreen service in 2019 were included. Prior images were excluded to simulate first-round screening. Eleven radiologists read DM and synthesized mammography with DBT (SM + DBT) images 4 weeks apart. Recall rates (RR), reading time, and diagnostic parameters were measured, and costs for screen reading and assessment were calculated.
Result: Among 65 clients studied, 13 were diagnosed with cancer, with concordant cancer recalls. SM + DBT reduced recall rates (RR), increased reading time, maintained cancer detection sensitivity, and significantly improved other diagnostic parameters, particularly false positive rates. Benign biopsy recalls remained equivalent. While SM + DBT screen reading cost was significantly higher than DM (DM AU$890 ± 186 vs SM + DBT AU$1279 ± 265; P < 0.001), the assessment cost (DM AU$29,504 ± 9427 vs SM + DBT AU$18,021 ± 5606; P < 0.001), and combined screen reading and assessment costs were significantly lower (DM AU$30,394 ± 9508 vs SM + DBT AU$19,300 ± 5721; P = 0.001). SM + DBT screen reading and assessment of 65 patients resulted in noteworthy cost savings (AU$11,094), equivalent to assessing 12 additional clients.
Conclusion: In first round screening, DBT yields significant cost savings by effectively reducing unnecessary recalls to assessment while maintaining diagnostic efficacy.
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http://dx.doi.org/10.1111/1754-9485.13664 | DOI Listing |
Jpn J Radiol
January 2025
Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer detection (CAD) and risk prediction. While AI CAD systems are slowly finding its way into clinical practice to assist radiologists or make independent reads, this review focuses on AI risk models, which aim to predict a patient's likelihood of being diagnosed with breast cancer within a few years after negative screening. Unlike AI CAD systems, AI risk models are mainly explored in research settings without widespread clinical adoption.
View Article and Find Full Text PDFSyst Rev
December 2024
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Objective: This systematic review update synthesized recent evidence on the benefits and harms of breast cancer screening in women aged ≥ 40 years and aims to inform the Canadian Task Force on Preventive Health Care's (CTFPHC) guideline update.
Methods: We searched Ovid MEDLINE® ALL, Embase Classic + Embase and Cochrane Central Register of Controlled Trials to update our searches to July 8, 2023. Search results for observational studies were limited to publication dates from 2014 to capture more relevant studies.
Indian J Radiol Imaging
January 2025
Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India.
Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis. To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population.
View Article and Find Full Text PDFCancers (Basel)
November 2024
National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.
Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images.
View Article and Find Full Text PDFJNCI Cancer Spectr
November 2024
Sydney Health Literacy Lab, School of Public Health, Faculty of Medicine and Health, The University of Sydney, NSW, Australia.
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