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Randall L. Barbour, Ph.D. SUNY Downstate Medical Center Brooklyn, New York

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.

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Randall L. Barbour, Ph.D. SUNY Downstate Medical Center Brooklyn, New York

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  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. 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. 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. 1.5e-8 0 -9.3e-9 Left (tumor) 2.1e-8 0 -1.2e-8 Right (healthy) 1 2 3 4 5 6 7 D Hbred [mol/l] Imaging frames 1 2 3 4 5 6 7 Dual Breast Imaging Result

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

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

  8. Measuring Heads 11a 11a 11a 11b 11b 11b 11b 11a 10a 10a 10a 10a 10b 10b 10b 10b Stepper Motor Controller Fiber optics 12 12 12 12 9b 9b 9b 9b 9a 9a 9a 9a (4,5)a (4,5)b (4,5)a (4,5)a (4,5)a (4,5)b (4,5)b (4,5)b 6b 6b 6b 6b 6a 6a 6a 6a Detection Unit Motor Controller Motor Controller Motor Controller Motor Controller PC Power Supply 8 8 8 8 Power Supply Power Supply Power Supply Instrumentation

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

  12. (IV) Position spatial integration Time series of spatial mean → O2 demand / metabolic responsiveness (II) Time 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 (I) Spatial  Temporal Averaging

  13. f2 f1 time 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

  14. Vasomotor Coordination Tumor Breast Healthy 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

  18. Subject Population

  19. Subject Population

  20. Patient Demographics

  21. New Patient’s Values X1 = .43; X2 = -.05 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 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. (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.) Performance of Multivariate Predictor Averages

  25. 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.

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