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      A tree augmented naive Bayesian network experiment for breast cancer prediction

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          Abstract

          In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related cancer registry records in the population of women across all ages. The aggregated results of our ten-fold cross validation method recommend a biopsy threshold higher than 2% for the aging population.

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          Most cited references 11

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          Ten-year risk of false positive screening mammograms and clinical breast examinations.

          The cumulative risk of a false positive result from a breast-cancer screening test is unknown. We performed a 10-year retrospective cohort study of breast-cancer screening and diagnostic evaluations among 2400 women who were 40 to 69 years old at study entry. Mammograms or clinical breast examinations that were interpreted as indeterminate, aroused a suspicion of cancer, or prompted recommendations for additional workup in women in whom breast cancer was not diagnosed within the next year were considered to be false positive tests. A total of 9762 screening mammograms and 10,905 screening clinical breast examinations were performed, for a median of 4 mammograms and 5 clinical breast examinations per woman over the 10-year period. Of the women who were screened, 23.8 percent had at least one false positive mammogram, 13.4 percent had at least one false positive breast examination, and 31.7 percent had at least one false positive result for either test. The estimated cumulative risk of a false positive result was 49.1 percent (95 percent confidence interval, 40.3 to 64.1 percent) after 10 mammograms and 22.3 percent (95 percent confidence interval, 19.2 to 27.5 percent) after 10 clinical breast examinations. The false positive tests led to 870 outpatient appointments, 539 diagnostic mammograms, 186 ultrasound examinations, 188 biopsies, and 1 hospitalization. We estimate that among women who do not have breast cancer, 18.6 percent (95 percent confidence interval, 9.8 to 41.2 percent) will undergo a biopsy after 10 mammograms, and 6.2 percent (95 percent confidence interval, 3.7 to 11.2 percent) after 10 clinical breast examinations. For every 100 dollars spent for screening, an additional 33 dollars was spent to evaluate the false positive results. Over 10 years, one third of women screened had an abnormal test result that required additional evaluation, even though no breast cancer was present. Techniques are needed to decrease false positive results while maintaining high sensitivity. Physicians should educate women about the risk of a false positive result from a screening test for breast cancer.
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            Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

            Despite trials of mammography and widespread use, optimal screening policy is controversial. To evaluate U.S. breast cancer screening strategies. 6 models using common data elements. National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. A contemporary population cohort. Lifetime. Societal. 20 screening strategies with varying initiation and cessation ages applied annually or biennially. Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. Varying test sensitivity or treatment patterns did not change conclusions. Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. National Cancer Institute.
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              Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

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                Author and article information

                Journal
                1506.05776

                Quantitative & Systems biology, Machine learning

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