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description
R.L. Barbour4/30/2014
Phenotype-Motivated Strategies for Optical Detection of Breast
Cancer
Randall L. Barbour, Ph.D.OSA, Miami April 30th, 2014
R.L. Barbour4/30/2014
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
R.L. Barbour4/30/2014
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
R.L. Barbour4/30/2014
Our Approach
Develop New Instrumentation
Apply Maneuvers+
Exploit principal features of tumor phenotype
Improved Detection of Breast Cancer
R.L. Barbour4/30/2014
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
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Diffuse Imaging: Diffuse Optical Tomography (DOT)
f0
Sourcef2
Oxyhemoglobin
Deoxyhemoglobin
Tumor
Detectors
f1f3 f4
Detectors
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Response to Compression
p
P P P P P
7
Oxyhemoglobin
Deoxyhemoglobin
Tumor
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Response to Carbogen
98% O2,2% CO2 98% O2,2% CO2 98% O2,2% CO2 98% O2,2% CO2
8
Oxyhemoglobin
Deoxyhemoglobin
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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.
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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
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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
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Opto-Mechanical Imaging
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Data Collection
Regulator and flow gauge
98 % O2,
2% CO2 5 L/min
Setup and baseline Articulation
Carbogen inspiration
Articulation
Craniocaudal articulation
Craniocaudal articulation
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Articulation Parameter Space
• Amplitude (1x, 2x)
• Duration (1, 2min)
• Rate (fast)
• Sequence (AB, BA)
Wave-likeQuasistatic
Loading-Unloading
Partial/uniform
Vibratory Creep
Mono-multiphasic
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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.
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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).
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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
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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
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Clinical Study• Resting State• Articulation• Carbogen
Hemodynamic Analysis3D image time series reconstructionBiomarker extraction: Bilateral comparison
Mechanical AnalysisYoung’s Modulus (Elasticity)Maxwell’s Model (Viscoelasticity)
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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
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Resting State Response
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Resting State Response
• Approach:
– Collect Baseline Time Series (~5 min)– Reconstruct 3D Image Time Series– Reduce Data Dimensionality: Integrate across
temporal/spatial domains
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Baseline Power Spectrum Density
N = 18N = 48
NO effect
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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
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Articulation Study
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Func
tiona
l
Structural
Mechanical
MRI
US
X-ray
TI
Elasto-graphy
CBE
Opt
ical
PET
L
Tumor
Opto- mechanical
Opto-Mechanical Imaging
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Fast Relaxation
Baseline
Elastic Compression Decompression
Recovery
Slow Relaxation
Tissue reaction to articulation
Force Relaxation
(Viscoelastic)
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Response to Articulation
Time [sec]
Dis
pla
cem
ent
[mm
]
Δ1
Δ2
Δ3
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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
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Protocol Guidance: Numerical Modeling – Hemodynamic Response
ΔHbTot
σ
Quasistatic wave-like Loading
FE-Bio, University of Utah
σ Effective Stress
Linear Elastic Model
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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)
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Young’s Modulus (Elastic)
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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
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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
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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
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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
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Carbogen Inspiration
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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
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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.
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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%).
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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.
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Volumetric Response
p= 0.047 p= 0.033
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Downsampling
Source/detector DetectorSource/detector Detector only43
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Results of downsampling
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