What do we do about mammographic density?

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What do we do about mammographic

density?

Sarah Vinnicombe

Clinical Senior Lecturer in Cancer Imaging Ninewells Hospital Medical School

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Risk Factors for Breast Cancer

• Genetic factors

– Single autosomal dominant genetic mutations

– Low penetrance single nucleotide polymorphisms

• ‘Environment’ and lifestyle factors

– Age at menarche, nulliparity, age at first FTP, breastfeeding, menopause, alcohol, hormones, BMI

• Radiation

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Risk Factors for Breast Cancer

• Genetic factors

– Single autosomal dominant genetic mutations

– Low penetrance single nucleotide polymorphisms

• ‘Environment’ and lifestyle

– Age at menarche, nulliparity, age at first FTP, breastfeeding, menopause, alcohol, hormones, BMI

• Radiation

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Clinical Importance of MD

Risk Population % Breast ca cases % x 10: BRCA 1, 2 0.5 5 x 4-10: FH++, LCIS 2-5 10 x 2-4: FH+, >50% density 20-40 50 x 0.5-2: FH, obesity, height 30-60 30

<0.5: non dense, early FTP, multiparity, breastfeeding 10-20 5

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Objectives

• Can we measure MD accurately & reliably enough?

• Does incorporation of MD into established risk models improve their predictive ability?

– Is its effect independent of other known risk factors?

• Are there other imaging markers of risk we should consider?

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Why does multimodal risk

stratification matter?

• Marmot report1:

– 4 cases overdiagnosed for every life saved – Screening should continue

• One size does not fit all

• How can screening be improved?

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Individualised Screening

Screening plan for a given woman:

– Whether screening is needed – When it should start

– Which modality

– Frequency of screening

– Age at which it should cease

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How should we measure MD?

• Visual (BI-RADS, quintiles, Tabar, Boyd SCC, VAS)

– Poor reproducibility & agreement1

• Semi-automatic thresholding: area-based % MD

– Cumulus (research tool)

• Automated volumetric methods

– Quantra, Volpara – SXA based methods

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Methods of Measurement

• Fully automated volumetric methods:

Quantra (Hologic) Volpara (Matakina)

• Raw data from FFDM images

• Volpara: determines ‘fat value’ & calculates height of fibroglandular tissue in each pixel • Quantra: uses calibration model to estimate

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Quantra

• Includes skin in estimation

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Volpara

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Issues: Automated Volumetric

methods

• Skin folds (overestimation dense volume)

• Very dense breasts (no fat  underestimation tissue volume)

• Very large breasts (>1 view needed) • Implants

• Are results understandable & plausible?

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Volumetric Methods

Correlation with visual assessment:

– BI-RADS Interobserver variability for 11 readers1

– Quantra compared with dichotomised BI-RADS – Best cut-off value for Quantra 22%

– Predicted 89% of all cases correctly

– Systematically lower values cf. visual classification

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Which Method should be used?

RMH/Bart’s Case-control study of six different methods:

• Cumulus, ImageJ (PD), Volpara, Quantra, SXA (FGV), BI-RADS • 685 ♀, attending for screening

• Valid readings: 100% with Volpara, 82% with Quantra • Volumetric methods: lower & smaller ranges

• Volpara < Quantra < SXA: medians 39ml, 71ml, 127ml • Correlations driven by agreement in breast size

• All were associated with known breast cancer risk factors in direction expected

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Do measurements depend on the

digital mammography unit?

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Results: Visual Assessment

• Visual scorings

– No evidence of unblinding bias (mixed vs. non-mixed) – 10 (10 %) of paired BI-BIRADS scores disagreed

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2009 GE Senographe DS 2010 Lorad Selenia

Examples

2009 GE Senographe DS 2010 Lorad Selenia

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Correlation with risk

RMH/Bart’s Case-control study: • 414 cases, 685 controls

• All methods: showed strong correlation between percent MD and risk, p for trend <0.0001

• Weaker correlations with Quantra

• Volumetric methods measure different parameters Dos Santos Silva, Allen, Vinnicombe et al.

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Correlation with risk

Shepherd et al., 2011:

• Case-control study 275 cases, 825 controls1:

• SXA adjusted for FH, BMI, breast biopsy, age FLB

• OR for breast cancer risk in highest & lowest quintiles

– 2.5 for % density – 2.9 for FGV

– 4.1 for % FGV

• FGV improved categorical risk classification in 20% Cancer Epidemiol Biomarkers Prev 2011

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Conclusions

• Individual volumetric methods:

– Repeatable, reliable

– Correlate well with risk

– Agreement between methods is poor! Volpara < Quantra < SXA

• Same tool should be used for given woman

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Research needed

• Absolute measures or percent measures??

– Conflicting results; absolute volume probably best1

– Does non-dense area matter?

• What cut-off for categorical classification?

– Quantra: 22% separated BI-RADS 1, 2 from 3, 4 2

– Volpara: 7.5% is cut-off for BI-RADS 2 and 3

1Keller B et al. SFBDW 2013 2Ciatto S et al. Breast 2012

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Can inclusion of MD improve

risk assessment models?

• MD highly heritable (twin/twin studies)1 - 60% of variance explained by genetic factors

• Only 15% of FH risk attributable to density2

• Inference: addition of MD to risk prediction

tools might improve their performance

1Boyd et al. NEJM 2002

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Risk Assessment Models and MD

Darabi H et al. Breast Cancer Res 2012:

• 1,022 cases, 868 controls with complete datasets • Cumulus for MD, subdivided into 6 categories

• Addition of MD, BMI & 18-SNP profile to Swedish

Gail increased c stat from 0.569 to 0.619 (Δ AUC 0.067 highly statistically significant)

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Risk Assessment Models and MD

Vachon et al., SFBDW 2013:

• Case-control study, 1643 cases, 2479 controls1

• 77-SNP & BI-RADS density equally assoc. with risk

• Addition of 77-SNP score to BCSC model

improved discrimination (AUC 0.69, Δ AUC 0.042)

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Conclusions

• Currently used models have only modest discriminatory ability

• Addition of MD to models (BI-RADS or Cumulus):

– only modest improvements in risk modelling

– Studies of addition of volumetric MD to better

calibrated risk assessment models (BOADICEA, Tyrer-Cuzick) are needed

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Other Imaging Markers of Risk

• DBT volumetry

• Texture – FFDM, DBT • MRI

– Fibroglandular volumes (FGV), water content – Texture

– Background parenchymal enhancement

• Whole breast ultrasound

• Optical spectroscopic imaging

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Alternative Modalities

• Digital breast tomosynthesis (DBT)

– more precise quantification of MD – reasonable correlation with FFDM – Good agreement with MR

volumes, r2 0.89

– FFDM measurements are greater across BI-RADS categories2

Tagliafico et al Breast Cancer Res Treat 2013 2Tagliafico A et al. Br J Radiol 2013

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Textural Analysis

Texture:

‘The distinctive physical composition or structure of something, especially with respect to the size, shape and arrangement of its parts’

• Image texture: mathematically described as spatial distribution of pixel intensities

• Difference between maximum and minimum pixel intensity, and spacing of peaks

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Coarse 0 50 100 150 200 250 G re y L e v e l

TA

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Mammographic texture

• 3 case–control studies1,2,3

• All yielded OR 2.8-14 for textural measures from SAR • Independent of MD

• MTR marker plus computerised MD gave highest OR (5.6) with AUC 0.661

• Only modest predictive power if added to MD model4

(digitised FSM)

1Nielsen M et al. Cancer Epidemiol 2010 2Wei et al. Radiology 2011 3Haberle et al. Breast Cancer Res 2012 4Manduca et al. Cancer Epidemiol Biomarkers Prev 2009

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DBT Texture

• No breast tissue overlap, better parenchymal visualisation

• Retroareolar ROIs, 2.5cm3 from DBT compared

with 2.5cm2 ROI from FFDM & PD (Cumulus)1

• Skewness, coarseness, contrast, energy

• DBT texture better correlated with PD than FFDM texture

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MRI: Volumetry

• MRI volumetry

– % water highly correlated with MD1

• Problems:

– defining chest wall – field inhomogeneity – time-consuming

– gold standard?? – needs automation!

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MRI

• Background parenchymal enhancement1 – 1275 women, 39 cancers

– BPE strongly correlated with risk

– OR >3 (mod/marked BPE vs. min/mild BPE)

– Present after correction for FGV

• Methods of automated measurement of FGV and BPE in development2

1King et al. Radiology 2011 2Wu S et al. SFBDW 2013

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New ACR BI-RADS Lexicon

New:

• fibroglandular tissue (a-d)

• background parenchymal enhancement (minimal-marked)

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Ultrasound

• Whole breast US1

• Tomographic acquistion

• Volume-averaged sound speed (VASS) • Comparator: MD on MMG

• VASS positively associated with MD

– negatively associated with non-dense area

– decreased with age, weight, menopausal status

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SWE

• Strain produced by US probe (shear waves)

• Shear wave propagation captured in real time • Quantifiable, good reproducibility

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Optical Methods

• Time domain diffuse optical spectroscopy1

• Measures water, lipid, collagen, oxy- and

deoxyhaemoglobin & scattering parameters

• Higher BI-RADS category associated with more water, collagen and less lipid

• More scattering with higher breast density

• Non-invasive method of assessing breast tissue composition

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Research Challenges

• The precise biological correlate of MD • Does non-dense volume matter?

• Are longitudinal measurements necessary?

• The relationship of MD, texture & spatial variation • Development of automated, calibrated & validated

volumetric methods - across vendors

- correlated with risk - including texture measures

• The place of other parameters (MRI, U/S, DOSI) • Incorporation into better risk assessment models

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Conclusions

• 10 years from now……

– Baseline assessment age 30

– FH, ethnicity, reproductive history, BMI – Blood/saliva test for SNP score

– Imaging measure of MD • ABUS, DOSI, MR if high risk – Computation of risk score

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Conclusions

• Imaging markers of risk (MD, texture, BPE) may:

– Define and quantify risk

• But in the future will also be used to:

– Predict likelihood of response to adjuvant and neoadjuvant treatments

– Indicate chances of successful chemoprevention – Inform on risk of developing

recurrent/metachronous breast cancer after successful Rx

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