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Biomedical Image Quantification www.biomediq.com

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Personalized Breast cancer ScreeningMads Nielsen

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Image Group

Biomediq

Image Group

Biomediq

Image Group

Biomediq

Image Group

Biomediq

Image Group

Biomediq

Image Group

Biomediq

Image Group

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Motivation and summary

In the western world women are mammography screened for breast cancer differentiated on age only using more than 100.000.000 mammograms per year.

Risk profiling may be used to personalize screening frequency and/or technology to reduce cost and increase detection rate.

Breast density may provide an essential risk profiling tool.

We present the Breast Cancer Risk Meter based on density and mammographic breast tissue texture doubling the risk segregation compared to breast density alone.

Patented technology is using the screening mammogram to automatically asses the 4 year risk of breast cancer.

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Participants

Biomediq A/S, Mads Nielsen• Management• Image analysis• Prototype development

DIKU, Chrstian Igel• Learning on massive data• Online learning

Capitol Region Screening Program, Ilse Vejborg• Data collection• Clinical testing

Department of Public Helath, Elsebeth Lynge• Risk modeling• Health economics

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BIRADS Examples of the 4 categories

1 2 3 4

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Method: BC Risk Meter

• Assess local pixel image structures [1]

• Match these to database of structures with known outcome (4y BC diagnosis) [2]

• Decide category for each pixel (e.g. healthy/4y BC diagnosis)

• Integrate local decisions to global score of the image

• Combine this with age and density into a risk estimate

1

2

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Local Image Structure

Local Image Features

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Method 3: Examples of density scores

Low Medium High

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Clinical studies overview

S1: • Purpose: to demonstrate risk segregation ability• Materials: 245 cases/250 controls form the Dutch screening program. • Digitized film 2-4 year prior to diagnosis. • Result: risk segregation capability adds to percentage densityS2: • Purpose: to independently verify sensitivity and demonstrate robustness• Materials: 226 cases/442 controls form the Minnesota Cohort.• Digitized film 3-6 year prior to diagnosis• Result: adds robustly a factor of two to risk segregation odds ratioS3: • Purpose: to demonstrate robustness to modality and estrogen receptor status• 145 cases/423 controls from the Pennsylvania Cohort.• Direct Digital Mammography on contralateral breast including estrogen

receptor status.• Result: verifies robustness to modality and shows relation to ER status

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S1: Breast cancer risk study

* Non-parametric

Method AUC p*

2 BI-RADS 0.58 < 10-2

3 Percentage 0.60 < 10-4

4 BC MTR 0.63 < 10-8

5 Aggregate 3+4 0.66 < 10-12

245 cancer cases (123 interval cancers and 122 screen detected) and 250 age-matched controls from the Dutch breast cancer screening program

Area under the ROC curve seperating cancers and controls

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S2: Breast cancer validation study

S2: Mammograms 3-6 years prior to diagnosisControls(N=442) Cases(N=226) P-value

Age 54.8 ± 10.5 55.8 ± 10.6 0.28BMI 27.9 ± 6.6 27.9 ± 5.5 0.96PD 18.4 ± 14.7 22.0 ± 15.4 0.003

S1: Mammograms 2-4 years prior to diagnosis

Controls(N=250) Cases(N=245) P-valueAge 61.3 ± 6.4 61.7 ± 8.8 0.19PD 13.2 ± 10.2 16.9 ± 11.1 <0.001

Adjusted for BMI, Age, Menopause, HRT

PD 0.61

BC MTR 0.60

PD + BC MTR 0.66

Result of recognizing texture from S1 in S2

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S3: Direct Digital Mammography

Examination at time of diagnosis, but from the unaffected mammogram.111 ER+ cases, 34 ER- cases, 423 controls.Differentiation of receptor status is based on recognition of texture in mammograms of known status.

Cancer vs control:

Separation of ER+ and ER-

AUC p-value

PD 0.56 <0.05

BC MTR 0.64 <0.001

PD + BC MTR 0.65 <0.001

AUC p-value

PD 0.51 NS

BC MTR 0.64 <0.05

PD + BC MTR 0.70 <0.001

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Simple simulation of BC MTR personalization of screening

PD is used for referral to high risk programs. PD is published to be expedient for screening frequency personalization.

These simulation are based on S1 and the 4 year prognosis instead of the relevant 2 year prognosis

Whenever 100,000 women are screened using a recall rate of app. 2%- 462 cancers will be detected- 189 cancers will not be detected but show before next round

By excluding 20 % of the women with lowest score in next round- 16 cancers extra will show as interval cancers- This is less than half the rate as in normal screening

Hence, the women not screened in next round will have the half false negative rate compared to whole population now.

By referring 10% to high risk analysis- 42 % of the interval cancers will be in this group

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Breast cancer technical details

New coordinate system with anatomical orientation

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Breast cancer technical details

Features used are:- 3 jet- Horizontal heterogenity at scales 1 mm, 2mm, 4mm, 8mm- Posisition

kNN-classification, k=100

20 X 1000 random points per image, and SFS of features = 20 committees

Fusion of 20.000 x 100 NN by average

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Project content

Retrospective study of all 2011 cancers at all time points8 x 4 x 2 x 360 images

Prospective study of all women screened in 2012-20152 x 4 x 160,000 images of 80 Mb each

Machine learning on massive data

Building a prototype

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Prototype

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Project conclusion

Will this technology make it possible to increase the early detection rate and save ressources at the same time?