What do we do about mammographic density?

What do we do about mammographic

density?

Sarah Vinnicombe

Clinical Senior Lecturer in Cancer Imaging Ninewells Hospital Medical School

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

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

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

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?

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?

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

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

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

Quantra

• Includes skin in estimation

Volpara

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?

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

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

Do measurements depend on the

digital mammography unit?

Results: Visual Assessment

• Visual scorings

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

2009 GE Senographe DS 2010 Lorad Selenia

Examples

2009 GE Senographe DS 2010 Lorad Selenia

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.

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

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

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

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

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

1_{Boyd et al. NEJM 2002}

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)

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)

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

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

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 2_{Tagliafico A et al. Br J Radiol 2013}

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

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

TA

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)

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

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

MRI: Volumetry

• MRI volumetry

– % water highly correlated with MD1

• Problems:

– defining chest wall – field inhomogeneity – time-consuming

– gold standard?? – needs automation!

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

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

New ACR BI-RADS Lexicon

New:

• fibroglandular tissue (a-d)

• background parenchymal enhancement (minimal-marked)

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

SWE

• Strain produced by US probe (shear waves)

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

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

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

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

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 R_x