1. 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. 2 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.) Corrosion Cast of Tumor Vasculature

3. 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. 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. 5 Dual Breast Imaging Result 1234567 1.5e-8 0 -9.3e-9 2.1e-8 0 -1.2e-8 Left (tumor) 1234567 Right (healthy)  Hbred [mol/l] Imaging frames

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

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

8. 8 Instrumentation

9. 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. 10 Power Spectrum of Hb Signal

11. 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 0 Hb deoxy Hb oxy (II) spatial integration meanSD scalar quantities (I)

12. 12 Spatial  Temporal Averaging Time Position (IV) spatial integration (II) (I) Time series of spatial mean → O 2 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. 13 1.Starting point is reconstructed image time series (IV) 2.Use (complex Morlet) wavelet transform as a time-domain bandpass filter operation A.Output is an image time series (IV) of amplitude vs. time vs. spatial position, for the frequency band of interest B.Filtered time series can be obtained for more than one frequency band 3.Recompute previously considered, but starting with the wavelet amplitude time series Method 2: Time-frequency (wavelet) analysis time f1f1 f2f2

14. 14 Vasomotor Coordination Healthy Breast Tumor Breast

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

16. 16 Provocation Analysis: Cancer case

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

18. 18 Subject Population Subject GroupBreast Pathology StatusN Age (yr) [mean ± SD] BMI (kg-m 2 ) [mean ± SD] Tumor Size [largest dimension] Clinical Description Retrospective Active CA1447.9 ± 12.328.7 ± 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 CA350.7 ± 9.430.4 ± 0.5— All had lumpectomies 2-3 yr prior to NIRS study Pre-CA0———— Non-CA Pathology1145.7 ± 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 941.6 ± 10.030.3 ± 7.2——

19. 19 Subject Population Prospective Active CA1451.4 ± 10.930.4 ± 4.5 5 ≤ 3 cm 9 > 3 cm (a 13 ductal carcinoma 1 axillary adenocarcinoma with mammary duct ectasia and hyperplasia Prior CA460.8 ± 9.325.5 ± 1.7— 3 prior ductal carcinoma 1 prior mucinous carcinoma All had lumpectomies 2-6 yr prior to NIRS study Pre-CA453.5 ± 3.4 29.0 ± 4.1 (N = 3) — 2 DCIS 1 atypical ductal hyperplasia 1 extremely dense breasts Non-CA Pathology643.7 ± 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 844.0 ± 6.830.5 ± 8.9——

20. 20 Patient Demographics

21. 21 Logistic Regression Applied Metrics Probability Metrics calculated and selected based on t-tests & ROC curves Metrics used as inputs into logistic regression model Logistic regression model calculates  i for each metric (X i ) Using  i, a predicted probability distribution can be created New patient’s X i used to generate probability of cancer in patient X 1 =.43; X 2 = -.05 New Patient’s Values Linear Model: P(cancer) = 0.75 Logistic Regression: P(cancer) = 0.90

22. 22 Scalar Metrics Examined

23. 23 Multivariate Predictor Performance

24. 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.)

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27. 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.