phenotype-motivated strategies for optical detection of breast cancer

R.L. Barbour4/30/2014

Phenotype-Motivated Strategies for Optical Detection of Breast

Cancer

Randall L. Barbour, Ph.D.OSA, Miami April 30th, 2014


DOT: Contrast Mechanisms for Tumor Detection

Static:• Intrinsic: (2-3x)

– Hb signal, Scattering– H2O, Lipid

Dynamic (Functional)• Intrinsic - Vascular Rhythms• Injectable dyes• Induced:

• Breast compression• Respiratory gases• Breathing maneuvers

Cancerous Healthy

Sluggish Perfusion

Reduced Oxygenation

Increased Total Hb


Hallmarks of Cancer

3

Evading* apoptosis

Self-sufficiency in growth signal

Insensitivity to anti-growth signals

Tissue* invasion and metastasis

Limitless replicative potential Enhanced*

angiogenesis

Increased Stiffness

↑ Hb Total

↓ HbO2 Sat

NO

Sustained Inflammatory Response

10:1

~3:1

Randall Barbour


Our Approach

Develop New Instrumentation

Apply Maneuvers+

Exploit principal features of tumor phenotype

Improved Detection of Breast Cancer


Phenotype Sensing Approach Maneuver

Increased Stiffness10:1

Visco-Elastic Measure Applied Compression - Articulation

↑Hbtotal3:1

Optical Applied Compression - Articulation

↓HbO2Sat Optical Carbogen Treatment

Up-regulation of NO Optical Resting State Measure(↑Vasomotion)

Tumor Detection Strategy


6

Diffuse Imaging: Diffuse Optical Tomography (DOT)

f0

Sourcef2

Oxyhemoglobin

Deoxyhemoglobin

Tumor

Detectors

f1f3 f4

Detectors


Response to Compression

p

P P P P P

7

Oxyhemoglobin

Deoxyhemoglobin

Tumor


Response to Carbogen

98% O2,2% CO2 98% O2,2% CO2 98% O2,2% CO2 98% O2,2% CO2

8

Oxyhemoglobin

Deoxyhemoglobin


LD1 LD2

Support

Arm

Motor Controller

Power Supply

Detector Module Detector Module

9

7

8

6

5

4

7 9

8

12

6 6

4 4

33

5 5

5

3

New Instrumentation

(1) laser beam combiner, (2) optical switch, (3) detector fibers, (4) sensing heads, (5) stepper motor drivers, (6) detection units, (7) servo motor controller, (8) personal computer, and (9) linear power supply. LD: Laser Diode.


10

Apply controlled mechanical provocationsExamine both breasts simultaneously

R. Al abdi, H.L. Graber, Y. Xu, and R.L. Barbour, "Optomechanical imaging system for breast cancer detection," J. Optical Society of America A, Vol. 28, pp. 2473-2493 (2011).

Design Goals


11

Support rods

Adjustable arcsStrain

reliefes

Steppermotors

Support rods

Adjustable arcsStrain

reliefes

Steppermotors

Articulating Sensing Head

Strain reliefs

Articulating Elements

64 D x 32 S (760 – 830 nm)/ measuring head = 8192 channels

2 Hz framing rate ~16KHz sampling rate


Opto-Mechanical Imaging


Data Collection

Regulator and flow gauge

98 % O2,

2% CO2 5 L/min

Setup and baseline Articulation

Carbogen inspiration

Articulation

Craniocaudal articulation

Craniocaudal articulation

13


Articulation Parameter Space

• Amplitude (1x, 2x)

• Duration (1, 2min)

• Rate (fast)

• Sequence (AB, BA)

Wave-likeQuasistatic

Loading-Unloading

Partial/uniform

Vibratory Creep

Mono-multiphasic

14


Available Data

• Optical Measures 760, 830 nm• Applied Force – strain gauge measure• Displacement

• Viscoelastic Response• Hemodynamic Response

+/- Respiratory Gases}

Hypothesis: Optomechanical sensing provides for improved

performance for breast cancer detection.


16

Hb Image Reconstruction

1 2

2

( ) ( )( ) ( ) ( )

( )i i

r i r ij jji

u uu W x

u

Normalized Difference Method:

u1 and u2 represent two measures at two different times

ur and Wr are computed from the reference model.

x is the difference between the optical properties of the target and the reference model.

W 12 cm x D 10 cm x H 6 cm

3908 voxel/pixel

Y. Pei, H.L. Graber, and R.L. Barbour, "Influence of systematic errors in reference states on image quality and on stability of derived information for DC optical imaging," Applied Optics, Vol. 40, pp. 5755-5769 (2001).


17

Validate System Performance

R.L. Barbour, R. Ansari, R. Al abdi, H.L. Graber, M.B. Levin, Y. Pei, C.H. Schmitz, and Y. Xu, "Validation of near infrared spectroscopic (NIRS) imaging using programmable phantoms," Paper 687002 in Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurements of Tissue

(Proceedings of SPIE, Vol. 6870), R.J. Nordstrom, Ed. (2008).

Torso phantom

Sensing head

BalloonPhantom

Dynamic Phantoms: Programmable Attenuation

LC Cells


18

Torso Phantom Experiment

True location of the LCC

0 20 40 60 80 100 120

-0.5

0

0.5

Inpu

t

0 20 40 60 80 100 1200.98

1

1.02

830n

m s

igna

l

0 20 40 60 80 100 1200.99

1

1.01

760n

m s

igna

l

Time [sec]

LCC: Liquid Crystal Cell


Clinical Study• Resting State• Articulation• Carbogen

Hemodynamic Analysis3D image time series reconstructionBiomarker extraction: Bilateral comparison

Mechanical AnalysisYoung’s Modulus (Elasticity)Maxwell’s Model (Viscoelasticity)


20

Parameter Cancer(N=23)

Benign Lesions(N=33)

Healthy Subjects(N=28)

Age (year) 56.7±11.2 52.2±9.7 53.8±11.8

BMI (kg/m2) 33.8±7.2 31.4±6.2 30.0±4.4

Menopausal status

Pre-menopausal 6 (27%) 17 (52%) 7 (25%)

Post-menopausal 17 (73%) 16 (48%) 21 (75%)

Race

Caucasian 3 (13%) 1 (3%) 3 (11%)

Hispanic 3 (13%) 7 (21%) 3 (11%)

African American 17 (74%) 24 (73%) 20 (71%)

Asian 0 (0%) 1 (3%) 2 (7%)

Subject Demographics

N = 84


Resting State Response


Resting State Response

• Approach:

– Collect Baseline Time Series (~5 min)– Reconstruct 3D Image Time Series– Reduce Data Dimensionality: Integrate across

temporal/spatial domains


23

Baseline Power Spectrum Density

N = 18N = 48

NO effect


Resting State Image: TSD

-5 0 5 10 15

-5 0 5 10 15

0 5 10 15 20

0 5 10 15 20

0 5 10 15 20

0 5 10 15 20

10

5

0

-5

10

5

0

-5

10

5

0

-5

0.3

0.25

0.2

0.15

0.1

0.05

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

R Axial L

R Axial L

R Sagittal L

R Sagittal L

R Coronal L

R Coronal L

15

10

5

0

-5

15

10

5

0

-5

10

5

0

-5

1 cm Tumor

4 cm Tumor

Metric

LC vs. NC, 2nd-Gen. Instrument,NCa = 12, NNon-Ca = 45

Hb Signal Component

AUC (%) Sens. (%) Spec. (%) # FPs # FNs

SMTSD HbSat 84.8 83.3 88.9 5 2

SSDTSD HbSat 85.7 83.3 91.1 4 2

TMSSD HbSat 85.4 83.3 88.9 5 2

Resting State Metric Performance

SM: Spatial Mean

TSD: Temporal Standard Deviation

SSD: Spatial Standard Deviation


Articulation Study


Func

tiona

l

Structural

Mechanical

MRI

US

X-ray

TI

Elasto-graphy

CBE

Opt

ical

PET

L

Tumor

Opto- mechanical

Opto-Mechanical Imaging


28

Fast Relaxation

Baseline

Elastic Compression Decompression

Recovery

Slow Relaxation

Tissue reaction to articulation

Force Relaxation

(Viscoelastic)


29

Response to Articulation

Time [sec]

Dis

pla

cem

ent

[mm

]

Δ1

Δ2

Δ3


2

,

: measured stress (Pa [N-m ])

: Young's modulus (Pa)

: time constant (s)

: time (s)

: viscosity (Pa-s)

t

E eE

E

t

Maxwell model for stress relaxation


31

Protocol Guidance: Numerical Modeling – Hemodynamic Response

ΔHbTot

σ

Quasistatic wave-like Loading

FE-Bio, University of Utah

σ Effective Stress

Linear Elastic Model


Force Relaxation (Viscoelastic)

0

5

10

15

20

25

30

35

40

Air 4.4 N Air 7.1 N Carbogen 4.4 N

Carbogen 7.1 N

Tim

e co

nst

ant

[sec

]

Cancer (N=16)

Benign (N=22)

Healthy (N=20)

32


Young’s Modulus (Elastic)

33

0.75

0.8

0.85

0.9

0.95

1

1.05

Cancer (N=16) Benign (N=22) Healthy (N=20)

Ela

sti

sit

y [

kP

a]

P-valuesCancer vs. All: 0.413Cancer vs. Benign: 0.331Cancer vs. Healthy: 0. 615


34

Mahalanobis Distance (MD)

1 THealthy Healthy Healthy Healthy Healthy Healthy

1 TCancer Cancer Healthy Healthy Cancer Healthy

,

,

k k k k k k

k k k k k k

x x x x

x x x x

C

C

1

2

Original data Normalized to the healthy breast


Articulation MD images

Right breast Left breast

50 y/o, BMI 44, 4 cm IDC in the left breastMD of (ΔHbTot,ΔHbDeoxy) 35

5-6 x increased contrast vs. static measures


36

HbTot-HbDeoxy

-50

0

50

100

150

200

250

300

350

Fullcompress.

Mediolateralrelaxation

Fullcompress.

Mediolateralrelaxation

MLcompress.

4.4 N Articulation 7.1 N Articulation

Nu

mb

er o

f p

ixel

s

Cancer (n = 16)

Benign (n = 22)

Healthy (n = 20)p = 0.002

p = 2.3x10-5

p = 0.005

p = 0.003

p = 0.005

Articulation MD


37

Carbogen Inspiration


38

Cancer - Right

Benign pathology -Right

Healthy

Right breast Left breast

Carbogen Inspiration MD

34 y/oBMI 291 cm IDC

48 y/oBMI 46Fibrocystic changes

43 y/oBMI 35Healthy

R.L. Barbour4/30/201439

Formulation Number of predictors

Number ofsubjects

SENS. (%)

SPEC. (%)

AUC (%) Method

Baseline 3 58 94 79 87 BLR

81 79 84 LOOCV

Articulation 2 58 81 93 93 BLR

81 90 85 LOOCV

Baseline and Articulation

5 58 88 100 96 BLR

82 93 87 LOOCV

Baseline, Articulation, and Carbogen

7 53 93 97 97 BLR

93 89 93 LOOCV

Summary of Clinical Performance

BLR: Binary Logistic Regression, LOOCV: Leave-Out-One Cross Validation, AUC: Area Under Curve.


Summary of Finding

Biomarkers extracted from controlled articulation, carbogen inspiration and resting dynamics all exhibit good diagnostic performance.Manipulation protocols yield superior tumor sizing and

localization. Multivariate predictors show excellent

diagnostic accuracy for detection of breast cancer (93%).

40


41

Future Directions

• Refine Protocols • Develop platform having reduced format• Correlation measures with gene expressions

– Improve performance of predictors for tumor recurrence, metastasis, sensitivity to chemotherapy etc.


Volumetric Response

p= 0.047 p= 0.033

42


Downsampling

Source/detector DetectorSource/detector Detector only43


Results of downsampling

44

phenotype-motivated strategies for optical detection of breast cancer

Documents

effects of compression

duration of compression

results of compression

blood reduction

breast tissue hemodynamics

breast cancer contrast

tumor detectionstatic

movement of tumor cells