A model‐based analysis of the hip project for breast cancer screening

Oortmarssen, G. J. Van; Habbema, J. Dik F.; Lübbe, J.; Maas, P.J. van der

doi:10.1002/ijc.2910460211

Cited by 40 publications

(26 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The MISCAN model was developed by van Oortmarssen et al 317 and is widely reported in the literature. The model is a specifically developed computer simulation package to, first, assess the clinical impact of different screening policies.…”

Section: Discussionmentioning

confidence: 99%

“…317 Using this method, the authors describe input parameters as continuous variables that change over time, the parameters of which are specified in mathematical functions which may be solved either analytically or numerically (by simulation). The type of data required by these more complex models are similar to those of event-based models, but are generally used in order to better describe the pre-clinical phases of the disease.…”

Section: Model Approachmentioning

confidence: 99%

See 1 more Smart Citation

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

118

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

Section: Discussionmentioning

confidence: 99%

Section: Model Approachmentioning

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

118

126

View full text Add to dashboard Cite

show abstract

“…The disease model is based on a three-state division of the development of invasive carcinoma and one DCIS state. Key parameters on the mean duration of the screen-detectable preclinical states and the sensitivity were derived from results of the Health Insurance Plan (HIP) analysis (van Oortmarssen et al, 1990b) and from a new analysis of all results from the Dutch screening trials in Nijmegen and Utrecht (de Koning et al, 1991). This method has been shown to be useful for predicting the effects and cost of screening in other countries, e.g.…”

Section: Methodsmentioning

confidence: 99%

Changes in use of breast-conserving therapy in years 1978-2000

Koning

Dongen²,

Maas³

1994

Br J Cancer

View full text Add to dashboard Cite

Summary The treatment of breast cancer patients has changed rapidly in the past decade, but empirical data at local and national level are scarce. Predicting the consequences of screening for primary treatment is consequently difficult. The aim of this analysis of records on admissions to hospital of women with breast cancer and/or for breast surgery together with a survey of all Dutch radiotherapy departments is to show the change in breast-conserving therapy and other primary treatment before the start of breast cancer screening in The Netherlands. There was a modest increase in breast-conserving therapy after 1981, coinciding with the first publication on its trial, followed by a sharp increase between 1985 and 1990, after the second publication. At the end of that 5 year period, 36% of all women with newly diagnosed invasive breast cancer underwent this type of surgery. Breast-conserving surgery is always followed by radiotherapy, but there has been a clear reduction in post-operative radiation after mastectomy. The percentage of breast-conserving therapy is at present higher in The Netherlands than in the USA. Implementing the Dutch screening programme will result in a maximum increase in breast-conserving therapy at national level of 34%, which stabilises at + 21%, or a 50% maximum increase at local level. The number of women treated by mastectomy will ultimately decrease by 9%. Given the rapidity of change towards the use of breast-conserving surgery, which is enhanced by screening, recent information will be needed in predicting capacity and assessing whether screen-detected women are treated adequately.The treatment of breast cancer patients has changed rapidly in the past decade. Several trials have led to a better understanding of the possible results of different types of primary treatment. Breast-conserving therapy, limited surgery followed by high-dose radiation, has been shown to be as effective as modified radical mastectomy for most operable patients (Veronesi et al., 1981). For mastectomy patients, routine post-operative radiotherapy is now usually considered unnecessary (Edland, 1988;Harris & Hellman, 1988). It could be assumed that treatment practice would be influenced by the information from these trials, but empirical data at national and local level are scarce in most countries (Farrow et al., 1992;Chouillet et al., 1994).At the same time, countries have started to implement breast cancer screening, as trials have shown that a reduction in mortality from breast cancer can be expected for screened women aged 50-69. The impact on the number and type of surgical procedures and radiotherapy will be great as a result of both the temporary increase in the number of women detected with breast cancer and the increase in early cancers (de Koning et al., 1990). Bottlenecks in capacity are expected, but there is a lack of empirical data to support this hypothesis.As we were especially uncertain about the development of breast-conserving therapy (outside a screening programme), we have analysed dat...

show abstract

“…'s T and W 0 dependent. There are indications in the literature [42,43] that primary breast tumors in older women tend to grow at a slower rate than in younger counterparts. We were among those who studied the possibility to extend the model by making the mean value of the growth rate λ dependent on the age of a patient at the time of tumor onset.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Identifiability of the joint distribution of age and tumor size at detection in the presence of screening

Hanin

Yakovlev

2007

Mathematical Biosciences

View full text Add to dashboard Cite

In recent years, a stochastic model of cancer development and detection allowing for arbitrary screening schedules has been developed and applied to analysis of screening trials and populationbased cancer incidence and mortality data. The model is entirely mechanistic, builds on a minimal set of biologically plausible assumptions, and yields the joint distribution of tumor size and age of a patient at the time of diagnosis. Whether or not parameters of the model can be estimated from data generated by cohort studies depends on model identifiability. The present paper provides a proof of this important property of the model.

show abstract

A model‐based analysis of the hip project for breast cancer screening

Cited by 40 publications

References 25 publications

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

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

Changes in use of breast-conserving therapy in years 1978-2000

Identifiability of the joint distribution of age and tumor size at detection in the presence of screening

Contact Info

Product

Resources

About