1. SUNY Downstate Medical Center
Derivation and Validation of Metrics for Breast Cancer Diagnosis from Diffuse Optical Tomography Imaging Data
Randall L. Barbour, Ph.D.
SUNY Downstate Medical Center
Brooklyn, New York

2. Corrosion Cast of Tumor Vasculature
Corrosion Cast of Tumor Vasculature. ‘tp’, = tumor periphery, ‘st’ = surrounding tissue. (M. Molls and P. Vaupel, Eds. Blood Perfusion and Microenvironment of Human Tumors: Implications for Clinical Radiooncology. Springer-Verlag, New York 2000.)

3. Basic Features of Tumor Vasculature
Leaky vessels
Increased interstitial pressure
Poorly developed vessels
altered/absence of normal control mechanisms
Relative state of hypoxemia
Dynamic optical studies should prove sensitive to multiple features of tumor biology.

4. Motivation For Dynamic Studies
Functional Parameters Associated with Blood Delivery to Tissue
Tissue Oxygen Demand
Vascular Compliance
Autoregulation (e.g., reactive hyperemia)
Autonomic Control (modulation of blood delivery)
Varying metabolic demand influences tissue-vascular coupling
Response to provocation
Influence of disease
Effects of drugs
Technical Benefits
Multiple features
High intrinsic contrast
No need for injection
Why Optical?
Simultaneous assessment of metabolic demand and vascular dynamics.

5. Dual Breast Imaging Result
-9.3e-9
2.1e-8
-1.2e-8
Left (tumor) 1 2 3 4 5 6 7
Right (healthy)
D Hbred [mol/l]
Imaging frames 1 2 3 4 5 6 7

6. Strategies for Data Analysis
Time Series Measures
Inherently information rich
Large dimensional
data sets.
To Obtain Useful Information: Consider the big picture

7. Dual Breast Imager Approx. Breast Positions Phantom Spheres
Gantry with
Opening
Fiberoptics
Measuring Cup Adjusters (Tilt, Lift, Pitch/Yaw)
Approx. Breast Positions

8. Instrumentation Measuring Heads Stepper Motor Controller Fiber optics8
Power Supply
Motor Controller
(4,5)b
(4,5)a 12
Detection Unit
Stepper Motor Controller PC
Fiber optics
Measuring Heads

9. Approach
Simple Idea:
Define utility of scalar metrics of amplitude, variance and spatial coordination of low frequency hemodynamics obtained from baseline measures
Amplitude response to a simple provocation
Simultaneous Measures: Paired difference

10. Power Spectrum of Hb Signal

11. Dimension Reduction: Temporal Spatial Averaging
Time
Position
(IV)
Spatial map of temporal standard deviation (SD)
(III)
Baseline temporal mean is 0, by definition
temporal integration
drop position information
sorted parameter value
100
Hbdeoxy
Hboxy
(II)
spatial integration
mean SD
scalar quantities
(I)

12. Spatial  Temporal Averaging
Time
Position
(IV)
spatial integration
(II)
(I)
Time series of spatial mean → O2 demand / metabolic responsiveness
Time series of spatial SD → Spatial heterogeneity
temporal integration
Temporal mean of spatial mean time series: 0, by definition
Temporal SD of spatial mean time series
Temporal mean of spatial SD time series
Temporal SD of spatial SD time series
scalar quantities

13. Method 2: Time-frequency (wavelet) analysis
Starting point is reconstructed image time series (IV)
Use (complex Morlet) wavelet transform as a time-domain bandpass filter operation
Output is an image time series (IV) of amplitude vs. time vs. spatial position, for the frequency band of interest
Filtered time series can be obtained for more than one frequency band
Recompute previously considered, but starting with the wavelet amplitude time series
time f1 f2

14. Vasomotor Coordination
Healthy Breast
Tumor Breast

15. Method 3: Provocation Analysis: Healthy Subject
Left breast (blue curve) and right breast (red curve)

16. Provocation Analysis: Cancer case

17. Scalar Metrics Explored
Experimental
Condition
Tumor-Associated
Phenotype
Scalar Metric
Resting State
Hypoxia
Spatial
Coordination
Evoked
Response
Angiogenesis

18. Breast Pathology Status
Subject Population
Subject Group
Breast Pathology Status N
Age (yr)
[mean ± SD]
BMI (kg-m2)
Tumor Size
[largest dimension]
Clinical Description
Retrospective
Active CA 14
47.9 ± 12.3
28.7 ± 5.3
10 ≤ 3 cm
4 > 3 cm
10 ductal carcinoma
1 ductal & lobular carcinoma
1 mucinous carcinoma
1 metastatic CA
1 inflammatory CA
Prior CA 3
50.7 ± 9.4
30.4 ± 0.5 —
All had lumpectomies 2-3 yr
prior to NIRS study
Pre-CA
Non-CA Pathology 11
45.7 ± 5.6
28.7 ± 5.5
(N = 7)
3 fibrocystic disease
4 breast cyst
1 axillary cyst
2 benign breast lumps
1 breast reduction surgery
No History
of Breast Pathology 9
41.6 ± 10.0
30.3 ± 7.2

19. Subject Population Prospective Active CA 14 51.4 ± 10.9 30.4 ± 4.5
51.4 ± 10.9
30.4 ± 4.5
5 ≤ 3 cm
9 > 3 cm(a
13 ductal carcinoma
1 axillary adenocarcinoma with
mammary duct ectasia
and hyperplasia
Prior CA 4
60.8 ± 9.3
25.5 ± 1.7 —
3 prior ductal carcinoma
1 prior mucinous carcinoma
All had lumpectomies 2-6 yr
prior to NIRS study
Pre-CA
53.5 ± 3.4
29.0 ± 4.1
(N = 3)
2 DCIS
1 atypical ductal hyperplasia
1 extremely dense breasts
Non-CA Pathology 6
43.7 ± 8.4
26.6 ± 4.9
(N = 4)
1 cystic disease
2 fibrocystic changes
1 fibrosis
1 benign breast lump
1 breast reduction surgery
No History
of Breast Pathology 8
44.0 ± 6.8
30.5 ± 8.9

20. Patient Demographics

21. Logistic Regression Applied
Metrics calculated and selected based on t-tests & ROC curves
Metrics used as inputs into logistic regression model
Probability
Logistic regression model calculates i for each metric (Xi)
Using i, a predicted probability distribution can be created
Metrics
X1 = .43; X2 = -.05
New Patient’s Values
New patient’s Xi used to generate probability of cancer in patient
Linear Model: P(cancer) = 0.75
Logistic Regression: P(cancer) = 0.90

22. Scalar Metrics Examined

23. Multivariate Predictor Performance

24. Performance of Multivariate Predictor Averages
(Two views of the same histogram.)
(This one has the same orientation as the ones in Figures 2 and 3.)
(This one is rotated 90°, so that all the bars are visible.)

27. Summary:
The amplitude and spatial coordination of the Hb signal is notable altered in tumor bearing breasts.
Multivariate metrics derived from simple scalar quantities derived from resting and provoked responses yield predictors having high discriminatory values.