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      Screening for Breast Cancer : Evidence Report and Systematic Review for the US Preventive Services Task Force

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          Abstract

          Importance

          Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear.

          Objective

          To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force.

          Data Sources

          MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024.

          Study Selection

          English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms.

          Data Extraction and Synthesis

          Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies.

          Main Outcomes and Measures

          Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms.

          Results

          Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I 2 = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers.

          Conclusions and Relevance

          Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

          Related collections

          Most cited references54

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          Is Open Access

          ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

          Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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            Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

            Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
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              • Record: found
              • Abstract: found
              • Article: not found

              Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement.

              Update of the 2009 U.S. Preventive Services Task Force (USPSTF) recommendation on screening for breast cancer.
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                Author and article information

                Journal
                JAMA
                JAMA
                American Medical Association (AMA)
                0098-7484
                April 30 2024
                Affiliations
                [1 ]Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Portland, Oregon
                [2 ]University of California Davis Center for Healthcare Policy and Research, Sacramento
                Article
                10.1001/jama.2023.25844
                c755ac2f-7be7-46fa-ab0f-8e6d5a912d4e
                © 2024
                History

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