Computer-aided Detection of Breast Cancer: A Cost-effectiveness Study

Lindfors, Karen K.; McGahan, Michele C.; Rosenquist, C. J.; Hurlock, Gregory S.

doi:10.1148/radiol.2392050670

Cited by 30 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, CAD (computer aided detection) appears to increase the sensitivity of breast cancer detection rates [37][38][39][40] and but increases recall and biopsy rates and the workload of the radiologist. Currently, 17% of Quality Standard Act approved mammographic sites are using CAD in the US [41].…”

Section: Discussionmentioning

confidence: 99%

Analysis of patients with false negative mammography and symptomatic breast carcinoma

Murphy

Dillon

Doherty

et al. 2007

Journal of Surgical Oncology

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis of patients with false negative mammography and symptomatic breast carcinoma

Murphy

Dillon

Doherty

et al. 2007

Journal of Surgical Oncology

View full text Add to dashboard Cite

show abstract

“…The analyses were based in a number of countries including the USA (n = 18 239,257,263,267,271,272,274,275,277,278,283,[289][290][291][292][293]295,298 ), the UK (n = 15 241,243,246,247,249,253,261,265,273,276,279,287,297,299,303 ), the Netherlands (n = 4 242,244,256,259 ), Hong Kong (n = 4 [304][305][306][307] ), Australia (n = 3 254,262,302 ), Italy (n = 3 245,251,252 ), Japan (n = 2 284,…”

Section: Study Overviewmentioning

confidence: 99%

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Evans

Astley

Stavrinos³

et al. 2016

Programme Grants Appl Res

117

126

View full text Add to dashboard Cite

BackgroundIn the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.ObjectivesTo (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.DesignTwo cohort studies assessing breast cancer incidence.SettingHigh-risk FHC and the NHSBSP Greater Manchester, UK.ParticipantsA total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].InterventionsQuestionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.Main outcome measuresBreast cancer incidence.Data sourcesThe NHSBSP; cancer registration.ResultsA total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.ConclusionsRisk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.Future workA pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

show abstract

“…Such modelling exercises have been carried out to assess the financial value of digital mammography and of CAD. 3,4 This paper presents a mathematical modelling exercise, which takes values from official UK statistics and the academic literature and simulates the impact of changing the sensitivity of screening.…”

Section: Introductionmentioning

confidence: 99%

“…[6] Subtracting the figure in [3] for screen-detected cancer from the total figure for new cancer cases gives a total figure for non-screen-detected cancer for each age group. 13 Subtracting from this total the number of women presenting via transition [5] in each year gives a figure for presenting cancers not detectable at screening.…”

mentioning

confidence: 99%

Modelling the Impact of Changes in Sensitivity on the Outcomes of the Uk Breast Screening Programme

Taylor

2010

J Med Screen

View full text Add to dashboard Cite

Objectives To assess the impact on breast cancer mortality of improving the sensitivity of breast screening programmes. Methods A Markov model was populated with data obtained from published statistics describing the UK National Health Service Breast Screening Programme and the incidence and mortality of breast cancer in the UK. The model was used to study the impact of changes to the sensitivity of screening. The effects on cancer detection rates and breast cancers and total mortality was studied for a cohort of women followed from age 45 to age 89. Results Running the model from age 45 to 89, with sensitivity set at the baseline value of 75%, predicts the detection at screening of 44 cancers per thousand of population and the detection outside screening of 82 cancers per thousand of population. Running the model with values of sensitivity from 75% to 95% shows the proportion of cancers detected at screening increasing as screening improves, and deaths from breast cancer falling. The drop in breast cancer deaths is however modest. Increasing sensitivity from 75% to 85% reduces the number of breast cancer deaths from 28 to 27 per thousand. Conclusions Likely achievable improvements in the sensitivity of screening do not have a marked effect on breast cancer mortality.

show abstract

Computer-aided Detection of Breast Cancer: A Cost-effectiveness Study

Abstract: The MCYLS is 19% greater for CAD added to screening versus screening mammography alone but is still within the accepted range for cost-effectiveness.

Cited by 30 publications

References 15 publications

Analysis of patients with false negative mammography and symptomatic breast carcinoma

Analysis of patients with false negative mammography and symptomatic breast carcinoma

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Modelling the Impact of Changes in Sensitivity on the Outcomes of the Uk Breast Screening Programme

Contact Info

Product

Resources

About