Factors Affecting Agreement between Breast Density Assessment Using Volumetric Methods and Visual Analogue Scales

Beattie, L.; Harkness, Elaine; Bydder, Megan; Sergeant, Jamie C.; Maxwell, Anthony; Barr, Nicky; Beetles, Ursula; Boggis, Caroline; Bundred, Sara; Gadde, Soujanya; Hurley, Edward J.; Jain, Anil K.; Lord, Elizabeth; Reece, Valerie; Wilson, Mary; Stavrinos, Paula; Evans, D Gareth; Howell, Tony; Astley, Susan M.

doi:10.1007/978-3-319-07887-8_12

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…For the cancer cases, the image including the cancer was included in the analysis. We have established that in the majority of cases the difference in volumetric density between mammographic images with cancer and the opposite (cancer-free) breast is small, 197 but the inclusion of diagnostic images showing cancer might have slightly increased the average density of the cases.…”

Section: Discussionmentioning

confidence: 82%

“…197 These analyses are described below, along with a method that we have developed which enables the adjustment of visual assessments to compensate for differences in practice between observers. 193 Finally, we have evaluated the potential of using automated measures of breast volume to predict self-reported weight and BMI; 198 the acquisition of weight data is problematic, and an automated, objective approach would be helpful.…”

Section: Analysis Of Density Datamentioning

confidence: 99%

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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

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118

126

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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.

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Section: Discussionmentioning

confidence: 82%

Section: Analysis Of Density Datamentioning

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

Self Cite

118

126

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“…VAS has been used in large clinical studies [ 30 , 31 , 32 , 33 , 34 ] and is considered preferable to some of the thresholding-based methods (described below) as it is less laborious and does not require specific reader training.…”

Section: Visual Methodsmentioning

confidence: 99%

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Destounis¹,

Arieno²,

Morgan³

et al. 2017

Diagnostics

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Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.

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“…Results indicated that there is a very strong correlation between the Cumulus PD and VBD, but there is less relationship between the Cumulus PD and the absolute volume of dense tissue. A recent study [ 179 ] has shown disagreement in measured volumetric density when using Quantra and Volpara based technologies. To date there are no reports of breast cancer associations for Volpara or Quantra measures of volumetric breast density techniques.…”

Section: Automatic Breast Density and Parenchymal Segmentationmentioning

confidence: 99%

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Juette

Denton

et al. 2015

International Journal of Breast Cancer

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Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

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Factors Affecting Agreement between Breast Density Assessment Using Volumetric Methods and Visual Analogue Scales

Cited by 3 publications

References 3 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

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

A Review on Automatic Mammographic Density and Parenchymal Segmentation

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