Patients are not given information about the risks, benefits, and radiation dose for a CT scan. Patients, ED physicians, and radiologists alike are unable to provide accurate estimates of CT doses regardless of their experience level.
We report and provide fitting functions for the abundance of dark matter halos and subhalos as a function of mass, circular velocity, and redshift from the new Bolshoi-Planck and MultiDark-Planck ΛCDM cosmological simulations, based on the Planck parameters. We also report halo mass accretion rates and concentrations. We show that the higher cosmological matter density of the Planck parameters compared with the WMAP parameters leads to higher abundance of massive halos at high redshifts. We find that the median halo spin parameter λ B = J( √ 2M vir R vir V vir ) −1 is nearly independent of redshift, leading to predicted evolution of galaxy sizes that is consistent with observations, while the significant decrease with redshift in median λ P = J|E| −1/2 G −1 M −5/2 predicts more decrease in galaxy sizes than is observed. Using the Tully-Fisher and Faber-Jackson relations between galaxy velocity and mass, we show that a simple model of how galaxy velocity is related to halo maximum circular velocity leads to increasing overprediction of cosmic stellar mass density as redshift increases beyond z ∼ 1, implying that such velocity-mass relations must change at z > ∼ 1. By making a realistic model of how observed galaxy velocities are related to halo circular velocity, we show that recent optical and radio observations of the abundance of galaxies are in good agreement with our ΛCDM simulations. Our halo demographics are based on updated versions of the Rockstar and Consistent Trees codes, and this paper includes appendices explaining all of their outputs. This paper is an introduction to a series of related papers presenting other analyses of the Bolshoi-Planck and MultiDark-Planck simulations.
IMPORTANCE Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. OBJECTIVE To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. MAIN OUTCOMES AND MEASUREMENTS Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. RESULTS Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive Յ12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. CONCLUSIONS AND RELEVANCE While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine (continued)
Background At least nineteen states have laws that require telling women with dense breasts and a negative screening mammogram to consider supplemental screening. The most readily available supplemental screening modality is ultrasound, yet little is known about its effectiveness. Objective To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasound screening for women with dense breasts. Design Comparative modeling with 3 validated simulation models. Data Sources Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; the medical literature. Target Population A contemporary cohort of women eligible for routine screening. Time Horizon Lifetime. Perspective Payer. Interventions Supplemental ultrasound screening for women with dense breasts following a negative screening mammogram. Outcome Measures Breast cancer deaths averted, quality-adjusted life years (QALYs) gained, false positive ultrasound biopsy recommendations, costs, costs per QALY gained. Results of Base-Case Analysis Supplemental ultrasound screening after a negative mammogram for women aged 50–74 with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models: 0.14–0.75), gained 1.7 QALYs (0.9–4.7), and resulted in 354 false-positive ultrasound biopsy recommendations (345–421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained ($112,000-$766,000). Restricting supplemental ultrasound screening to women with extremely dense breasts cost $246,000 per QALY gained ($74,000-$535,000). Results of Sensitivity Analysis The conclusions were not sensitive to ultrasound performance characteristics, screening frequency, or starting age. Limitations Provider costs for coordinating supplemental ultrasound were not considered. Conclusions Supplemental ultrasound screening for women with dense breasts undergoing screening mammography would substantially increase costs while producing relatively small benefits in breast cancer deaths averted and QALYs gained. Primary Funding Source National Institutes of Health
Background Estimates of radiation-induced breast cancer risk from mammography screening have not previously considered dose exposure variation or diagnostic work-up after abnormal screening. Objective To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening, considering exposure from screening and diagnostic mammography and dose variation across women. Design Two simulation-modeling approaches using common data on screening mammography from the Breast Cancer Surveillance Consortium and radiation dose from mammography from the Digital Mammographic Imaging Screening Trial. Setting U.S. population. Patients Women aged 40–74 years. Interventions Annual or biennial digital mammography screening from age 40, 45, or 50 until 74. Measurements Lifetime breast cancer deaths averted (benefits) and radiation-induced breast cancer incidence and mortality per 100,000 women screened (harms). Results On average, annual screening of 100,000 women aged 40 to 74 years was projected to induce 125 breast cancers (95% confidence interval [CI]=88–178) leading to 16 deaths (95% CI=11–23) relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 radiation-induced breast cancers leading to 32 deaths per 100,000 women. Women with large breasts requiring extra views for complete breast examination (8% of population) were projected to have higher radiation-induced breast cancer incidence and mortality (266 cancers, 35 deaths per 100,000 women), compared to women with small or average breasts (113 cancers, 15 deaths per 100,000 women). Biennial screening starting at age 50 reduced risk of radiation-induced cancers 5-fold. Limitations We were unable to estimate years of life lost from radiation-induced breast cancer. Conclusions Radiation-induced breast cancer incidence and mortality from digital mammography screening are impacted by dose variability from screening and resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may be at higher risk of radiation-induced breast cancer; however, the benefits of screening outweigh these risks.
Purpose To compare the performance of digital breast tomosynthesis (DBT) and two-dimensional synthetic mammography (SM) with that of digital mammography (DM) in a population-based mammographic screening program. Materials and Methods In this prospective cohort study, data from 37 185 women screened with DBT and SM and from 61 742 women screened with DM as part of a population-based screening program in 2014 and 2015 were included. Early performance measures, including recall rate due to abnormal mammographic findings, rate of screen-detected breast cancer, positive predictive value of recall, positive predictive value of needle biopsy, histopathologic type, tumor size, tumor grade, lymph node involvement, hormonal status, Ki-67 level, and human epidermal growth factor receptor 2 status were compared in women who underwent DBT and SM screening and in those who underwent DM screening by using χ tests, two-sample unpaired t tests, and tests of proportions. Results Recall rates were 3.4% for DBT and SM screening and 3.3% for DM screening (P = .563). DBT and SM screening showed a significantly higher rate of screen-detected cancer compared with DM screening (9.4 vs 6.1 cancers per 1000 patients screened, respectively; P < .001). The rate of detection of tumors 10 mm or smaller was 3.2 per 1000 patients screened with DBT and SM and 1.8 per 1000 patients screened with DM (P < .001), and the rate of grade 1 tumors was 3.3 per 1000 patients screened with DBT and SM versus 1.4 per 1000 patients screened with DM (P < .001). On the basis of immunohistochemical analyses, rates of lymph node involvement and tumor subtypes did not differ between women who underwent DBT and SM screening and those who underwent DM screening. Conclusion DBT and SM screening increased the detection rate of histologically favorable tumors compared with that attained with DM screening. RSNA, 2018 Online supplemental material is available for this article.
Purpose To evaluate the comparative effectiveness of combined biennial digital mammography and tomosynthesis screening, compared to biennial digital mammography screening alone, among women with dense breasts. Materials and Methods We used an established, discrete-event breast cancer simulation model to estimate the comparative clinical effectiveness and cost-effectiveness of biennial screening with both digital mammography and tomosynthesis versus digital mammography alone among U.S. women ages 50–74 years with dense breasts from a federal payer perspective and a lifetime horizon. We estimated input values for test performance, costs, and health state utilities from the National Cancer Institute’s Breast Cancer Surveillance Consortium, Medicare reimbursement rates, and the medical literature. We performed sensitivity analyses to determine the implications of varying key model parameters, including combined screening sensitivity and specificity, transient utility decrement of diagnostic work-up, and the additional cost of tomosynthesis. Results For our base case analysis, the incremental cost per quality-adjusted life year (QALY) gained by adding tomosynthesis to digital mammography screening was $53,893. An additional 0.5 deaths were averted and 405 false-positives were avoided per 1,000 women after 12 rounds of screening. Combined screening remained cost-effective (less than $100,000 per QALY gained) over a wide range of incremental improvements in test performance. Overall, cost-effectiveness was most sensitive to the additional cost of tomosynthesis. Conclusion Biennial combined digital mammography and tomosynthesis screening for U.S. women aged 50–74 years with dense breasts is likely to be cost-effective if priced appropriately (≤ $226 combined exams versus $139 for digital mammography alone) and if reported interpretive performance metrics of improved specificity with tomosynthesis are met in routine practice.
There is increasing interest in the potential benefits and harms of screening ultrasound to supplement mammographic screening of women with dense breast tissue. We review the current evidence regarding adjunctive screening breast ultrasound (US) and provide a summary for clinicians who counsel patients with dense breasts. We conducted a comprehensive literature review of published clinical trials and observational cohort studies assessing the efficacy of screening handheld US (HHUS) and automated breast US (ABUS) to supplement mammography among women with dense breasts. From a total of 189 peer-reviewed publications on the performance of screening US, 12 studies were relevant to our analysis. The reporting of breast cancer risk factors varied across studies; however, the study populations tended to be at greater than average risk for developing breast cancer. There is consistent evidence that adjunctive screening US detects more invasive cancers compared to mammography alone, but there is currently no evidence of associated long-term breast cancer mortality reduction. The studies also collectively found that US was associated with an additional 11.7–106.6 biopsies/1,000 examinations (Median 52.2), and detected an additional 0.3–7.7 cancers/1,000 examinations (Median 4.2). The associated number of unnecessary breast biopsies resulting from adjunct US screening exceeds that observed with screening mammography alone by approximately 5-fold. Adjunctive screening with ultrasound should also be considered in the context of screening mammography. It is important for clinicians to be aware that improvements in cancer detection in mammographically dense breasts have been achieved with the transition from film to digital mammography, reducing a limitation of film mammography. Clinicians should discuss breast density as one of several important breast cancer risk factors, consider the potential harms of adjunctive screening, and arrive at a shared decision consistent with each woman’s preferences and values.
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