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4/30/2014R.L. Barbour Phenotype-Motivated Strategies for Optical Detection of Breast Cancer Randall L. Barbour, Ph.D. OSA, Miami April 30th, 2014.

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Presentation on theme: "4/30/2014R.L. Barbour Phenotype-Motivated Strategies for Optical Detection of Breast Cancer Randall L. Barbour, Ph.D. OSA, Miami April 30th, 2014."— Presentation transcript:

1 4/30/2014R.L. Barbour Phenotype-Motivated Strategies for Optical Detection of Breast Cancer Randall L. Barbour, Ph.D. OSA, Miami April 30th, 2014

2 4/30/2014R.L. Barbour 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

3 4/30/2014R.L. Barbour 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 Cancer cells are usually insensitive to anti growth signals, they divide in the absence of proper signals, and they lose their capability of programmed cell death, which is called apoptosis. Because of that, cancer cells have the potential to replicate themselves limitlessly and generate vast cell populations. These abnormalities can lead to the formation of tumors. The movement of tumor cells to neighboring and distant parts of the body is known as metastasis. In order for tumors to grow beyond a few mm it needs new blood supply, which is called angiogenesis. Increased Stiffness ↑ Hb Total ↓ HbO 2 Sat NO Sustained Inflammatory Response 10:1 ~3:1

4 4/30/2014R.L. Barbour Our Approach Develop New Instrumentation Apply Maneuvers + Exploit principal features of tumor phenotype Improved Detection of Breast Cancer

5 4/30/2014R.L. Barbour PhenotypeSensing ApproachManeuver Increased Stiffness 10:1 Visco-Elastic MeasureApplied Compression - Articulation ↑Hbtotal 3:1 OpticalApplied Compression - Articulation ↓HbO2SatOpticalCarbogen Treatment Up-regulation of NOOpticalResting State Measure (↑Vasomotion) Tumor Detection Strategy

6 4/30/2014R.L. Barbour 6 Diffuse Imaging: Diffuse Optical Tomography (DOT) DOT employs diffuse light that propagates through tissue, at multiple projections, to yield three- dimensional quantified tomographic images of the internal optical properties of organs. Light propogate in scattering medium in a banana shape path. 00 Source 22 Oxyhemoglobin Deoxyhemoglobin Tumor Detectors 11 33 44

7 4/30/2014R.L. Barbour Response to Compression This simple schematics explains the effects of compression on breast tissue hemodynamics. Blood reduction is expected as a results of compression. Because of the increase resistance to flow, enhanced stiffness, and poor perfusion in tumor, the blood reduction in tumor is expected to be slow, and this enhances its contrast. Blood reduction depends on the type, amplitude, and duration of compression. Thus, rich information can be extracted from studying hemodynamic responses to different types of compressions. p P P P P P 7 Oxyhemoglobin Deoxyhemoglobin Tumor

8 4/30/2014R.L. Barbour Response to Carbogen $ Carbogen is a gas mixture of oxygen and carbon dioxide. Carbogen Breathing increases tissue oxygen saturation. Since tumors have high total hemoglobin concentration with low oxygen saturation, breathing carbogen is expected to increase their oxygen saturation relatively higher than normal tissue, and this will enhance breast cancer contrast. 98% O 2,2% CO 2 8 Oxyhemoglobin Deoxyhemoglobin

9 4/30/2014R.L. Barbour LD1 LD2 Support Arm Motor Controller Power Supply Detector Module 9 7 8 6 5 4 79 8 1 2 66 44 33 55 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. $ The MH should do four functions: 1.Support stable optodes contact with breasts. 2.Accommodate a wide range of breast sizes. 3.Measure the biomechanical properties of breast tissue. 4.Apply controlled articulations

10 4/30/2014R.L. Barbour 10 Apply controlled mechanical provocations Examine both breasts simultaneously The core of my research was to $ The simultaneous scanning of both breasts is an important feature, because the optical images of the contralateral healthy breast will be used as a reference. I published a paper about this instrument in the Journal of Optical Society of America A. 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 4/30/2014R.L. Barbour 11 Articulating Sensing Head A clam-shell and articulating elements design which is similar to human hand was used to accommodate a wide range of breast sizes and to apply pressure provocations. Optical scan is done in the seated position, where patient should be the most comfortable. In this design a uniform pressure can be applied, and the articulating elements can conform to the breast shape. Strain reliefs Articulating Elements 64 D x 32 S (760 – 830 nm)/ measuring head = 8192 channels 2 Hz framing rate ~16KHz sampling rate

12 4/30/2014R.L. Barbour Opto-Mechanical Imaging To summarize, the main three $ Combinations of these domains can yield either additive or wholly new information, depending on whether one domain interacts with the other. $ In this report, we described a new approach to breast imaging based on the interaction between controlled applied mechanical force and tissue hemodynamics.

13 4/30/2014R.L. Barbour Data Collection Both breasts were placed inside the sensing heads, and then a baseline pressure of approximately 0.4 lb (1.8 N) was applied. A baseline scan of about 5-10 minutes was collected with the patient at rest. Then an automatic control was activated to apply a set of pressure provocations (10 min). $ 5 minutes after that, participant was given a facemask to breath Carbogen gas, wait for 5-10 min, and then a second set of pressure provocations was applied while patient was breathing Carbogen. Regulator and flow gauge 98 % O 2, 2% CO 2 5 L/min Setup and baseline Articulation Carbogen inspiration Articulation Craniocaudal articulation 13

14 4/30/2014R.L. Barbour Articulation Parameter Space Amplitude (1x, 2x) Duration (1, 2min) Rate (fast) Sequence (AB, BA) Wave-like Quasistatic Loading- Unloading Partial/uniform VibratoryCreep Mono-multiphasic 14

15 4/30/2014R.L. Barbour 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 4/30/2014R.L. Barbour 16 Hb Image Reconstruction Normalized Difference Method: u 1 and u 2 represent two measures at two different times u r and W r are computed from the reference model.  x is the difference between the optical properties of the target and the reference model. The picture is for the finite element model that we used for image reconstruction, It has similar shape and size of a real breast inside the sensing heads. The normalized difference method was used to reconstruct changes in background optical properties. Then the changes in the background optical properties were transformed into changes in hemoglobin concentrations using the molar extinction coefficients of oxyhemoglobin and deoxyhemoglobin. 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 4/30/2014R.L. Barbour 17 Validate System Performance The second part of specific aim 2 is to $ The critical need served by phantoms is that they essentially are the only way to conduct imaging studies where “the right answer” is known a priori. The balloon phantom was built to evaluate and quantify the effects of breast deformation and size variation on the measured optical signals and image reconstruction algorithms. It was constructed using a latex balloon filled with intra- lipid and India ink solution, and its size was changed by pumping more fluid. A liquid crystal cell was fixed at the center to produce dynamic contrast. 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 Balloon Phantom Dynamic Phantoms: Programmable Attenuation LC Cells

18 4/30/2014R.L. Barbour 18 Torso Phantom Experiment True location of the LCC These results was obtained from driving the LCCs inside the torso-phantom with a square wave. Shown in the left figure are tracings of the input driving function and the recovered optical signals at both wavelengths 760 nm and 830 nm. The high contrast area coincide with true location of the LCCs. LCC: Liquid Crystal Cell

19 4/30/2014R.L. Barbour Clinical Study Resting State Articulation Carbogen Hemodynamic Analysis 3D image time series reconstruction Biomarker extraction: Bilateral comparison Mechanical Analysis Young’s Modulus (Elasticity) Maxwell’s Model (Viscoelasticity)

20 4/30/2014R.L. Barbour 20 An IRB approved clinical study was conducted on 28 healthy volunteers, 23 breast cancer patients, and 33 women with benign breast lesions to assess the imager’s efficacy for detecting breast cancer lesions. ParameterCancer (N=23) Benign Lesions (N=33) Healthy Subjects (N=28) Age (year)56.7±11.252.2±9.753.8±11.8 BMI (kg/m 2 )33.8±7.231.4±6.230.0±4.4 Menopausal status Pre-menopausal6 (27%)17 (52%)7 (25%) Post-menopausal17 (73%)16 (48%)21 (75%) Race Caucasian3 (13%)1 (3%)3 (11%) Hispanic3 (13%)7 (21%)3 (11%) African American17 (74%)24 (73%)20 (71%) Asian0 (0%)1 (3%)2 (7%) Subject Demographics N = 84

21 4/30/2014R.L. Barbour Resting State Response

22 4/30/2014R.L. Barbour Resting State Response Approach: – Collect Baseline Time Series (~5 min) – Reconstruct 3D Image Time Series – Reduce Data Dimensionality: Integrate across temporal/spatial domains

23 4/30/2014R.L. Barbour 23 Group means and standard errors of PDs of power spectrum density (PSD) that was calculated from SM{HbTot} in baseline. The benign-pathology and healthy subjects are combined in one group (green curve). The error bars are the standard errors across subjects (inter-patients). Significant differences between cancer group compared to benign-pathology and healthy subjects group were found at low and high frequency band (p<0.01) (0.05- 0.1 Hz, 0.5-0.7 Hz). Baseline Power Spectrum Density N = 18 N = 48 NO effect

24 4/30/2014R.L. Barbour Resting State Image: TSD -5 0 5 10 15 0 5 10 15 20 10 50 -5 10 50 -5 10 50 -5 R Axial L R Sagittal L R Coronal L 1510 50 -5 1510 50 -5 10 50 -5 1 cm Tumor 4 cm Tumor

25 Metric LC vs. NC, 2 nd -Gen. Instrument, N Ca = 12, N Non-Ca = 45 Hb Signal Component AUC (%) Sens. (%)Spec. (%)# FPs# FNs SMTSDHbSat84.883.388.952 SSDTSDHbSat85.783.391.142 TMSSDHbSat85.483.388.952 Resting State Metric Performance SM: Spatial Mean TSD: Temporal Standard Deviation SSD: Spatial Standard Deviation

26 4/30/2014R.L. Barbour Articulation Study

27 4/30/2014R.L. Barbour Functional Structural Mechanical MRI US X-ray TI Elasto- graphy CBE Optical PET L Tumor Opto- mechanical Opto-Mechanical Imaging

28 4/30/2014R.L. Barbour 28 Fast Relaxation Baseline Elastic Compression Decompression Recovery Slow Relaxation Tissue reaction to articulation Force Relaxation (Viscoelastic) In this slide shows tissue reaction to a 7.1 N quasastatic compression. four phases of mechanical provocations $ The force relaxation and recovery phenomena are intrinsic properties of visco-elastic material of breast tissue.

29 4/30/2014R.L. Barbour 29 Computed path-length from the force relaxation after a 7.1 N full compression. Change in Hb signals were calculated in the late period of stress relaxation, where path-length changes between sources and detectors were very low. Thus, measured optical signals highly represent hemodynamic responses of the breast. I did the same analysis during the force recovery period. Response to Articulation Time [sec] Displacement [mm] Δ1Δ1 Δ2Δ2 Δ3Δ3

30 4/30/2014R.L. Barbour Maxwell model for stress relaxation

31 4/30/2014R.L. Barbour 31 Protocol Guidance: Numerical Modeling – Hemodynamic Response ΔHb Tot σ To study the interaction of controlled applied mechanical provocation on tissue hemodynamics, a linear-elastic finite element analysis on a homogeneous poroelastic tissue model was used to predict the effect of compression on the internal forces, and to work as a protocol guidance The spatial maps of the effective stress for a wave-like compression (from left to right), and reconstructed changes in total hemoglobin are shown below. HbTot images are from a healthy participant, who was 43 years old with size D breast and BMI of 35. Notice the correlation between blood exclusion and the computed effective stress (blood move from high pressure regions to the low pressure regions). Quasistatic wave-like Loading FE-Bio, University of Utah σ Effective Stress Linear Elastic Model

32 4/30/2014R.L. Barbour Force Relaxation (Viscoelastic) The group means and standard errors of the average time constants during slow force relaxation are shown. No significant difference between cancer group compared to benign-pathology and healthy groups. The average value of slow relaxation is 30.0±1.8 seconds, which consistant to what was reported by Carp et al in 2009. 32

33 4/30/2014R.L. Barbour Young’s Modulus (Elastic) The group means and standard errors of the average young’s modulus (stiffness) during 7.1 N compression. No significant difference between cancer group compared to other category groups: benign-pathology, healthy groups, or both together. 33 P-values Cancer vs. All: 0.413 Cancer vs. Benign: 0.331 Cancer vs. Healthy: 0. 615

34 4/30/2014R.L. Barbour 34 Mahalanobis Distance (MD) To extend this normalization method to be used into two Hb signals, The Mahalanobis distance, which is analogue of univariate z-score, was used. It permits straightforward evaluation of each data point’s statistical distance from the mean value of the two Hb signals. MD was calculated in three steps: $ 1.Subtracting the mean. 2.Projecting the results into the eigenvectors of the two Hb signals covariance matrix 3.Dividing the results by their standard deviation in the healthy breast. Original data Normalized to the healthy breast

35 4/30/2014R.L. Barbour 3D images (coronal, sagittal and axial) of the calculated MD from a subject with breast cancer. MD was calculated from (ΔHbTot,ΔHbDeoxy) during the 4.4 N mediolateral relaxation. The subject was 50 year old, with size D breasts, and BMI of 44, and had a 4 cm invasive ductal carcinoma in the left breast. Normalization using Mahalanobis Distance enhance image contrast These contrasts are 5-6 times those seen in optical static images. Articulation MD images Right breast Left breast 50 y/o, BMI 44, 4 cm IDC in the left breast MD of (ΔHbTot,ΔHbDeoxy) 35 5-6 x increased contrast vs. static measures

36 4/30/2014R.L. Barbour 36 p = 0.002 p = 2.3x10 -5 p = 0.005 p = 0.003 p = 0.005 Group means and standard errors of PDs in number of pixels that have MD greater than 5.5. Measurements were done after 4.4 N full compression, 4.4 N mediolateral relaxation, 7.1 N full compression, 7.1 N mediolateral relaxation, and 7.1 N mediolateral compression. Mahalanobis distances were calculated from the (ΔHbTot,ΔHbDeoxy). The error bars are the standard errors across subjects (inter-patients). Significant differences (p<0.01) between cancer group compared to benign-pathology and healthy groups were found under all types of articulations with smallest p-value for 4.4 N mediolateral relaxation (p= 7.8 x 10- 5). Articulation MD

37 4/30/2014R.L. Barbour 37 Spatial mean time series of HbSat and HbTot responses to carbogen inspiration. Subject was 65 years old, with a BMI of 29 and size D breasts, and she had a 2.5 cm invasive ductal carcinoma tumor in the left breast. Red dots on the graphs indicate the start of carbogen breathing. The difference between the values of tissue oxygen saturation before and after Carbogen inspiration (Δ) were used to produce breast cancer predictors. Carbogen Inspiration

38 4/30/2014R.L. Barbour 38 Cancer - Right Benign pathology -Right Healthy Right breast Left breast Coronal sections of the MDs thresholded at 5.5 for (ΔHbTot,ΔHbSat) Cancer patient was 34 y/o with BMI of 29 and 1-cm Invasive Ductal Carcinoma in the right breast at 4 clk. Benign pathology subject was 48 y/o, with BMI of 46, and Fibrocystic changes and microcalcification in the right breast in UOQ. Healthy: 43 y/o, with BMI of 35. Carbogen Inspiration MD 34 y/o BMI 29 1 cm IDC 48 y/o BMI 46 Fibrocystic changes 43 y/o BMI 35 Healthy

39 4/30/2014R.L. Barbour 39 FormulationNumber of predictors Number of subjects SENS. (%) SPEC. (%) AUC (%) Method Baseline358947987BLR 817984LOOCV Articulation2588193 BLR 819085LOOCV Baseline and Articulation 5588810096BLR 829387LOOCV Baseline, Articulation, and Carbogen 7539397 BLR 938993LOOCV Cancer predictors derived from the three protocols, baseline, articulation and carbogen inspiration, have shown their potential to diagnose breast cancer. BLR was used to compute different multivariate predictors. In this table an example of four multivariate predictors and their ROC results are shown. Diagnostic accuracy of 93% was achieved by combining predictors from baseline, articulation, and carbogen inspiration. Summary of Clinical Performance BLR: Binary Logistic Regression, LOOCV: Leave-Out-One Cross Validation, AUC: Area Under Curve.

40 4/30/2014R.L. Barbour 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 4/30/2014R.L. Barbour 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.

42 4/30/2014R.L. Barbour Volumetric Response p= 0.047 p= 0.033 Group means and standard errors of PDs in number of pixels that have MD greater than 5.5 during baseline, Articulation, and Carbogen breathing. The group mean of the number of pixels during baseline are significantly greater than those during articulation and carbogen breathing. This indicate that the contrast during baseline was diffused, extended beyond the margin of the tumor, and involve a large percentage of tumor bearing breast. These results suggest that the diagnostic power for breast cancer during baseline can be measured with low density optical sensors. 42

43 4/30/2014R.L. Barbour Downsampling Source/detector Detector Source/detector Detector only 43

44 4/30/2014R.L. Barbour Results of downsampling 44 To evaluate if the downsampling can work, Computed the percentage of channels that have higher temporal variance in the tumor bearing breast than the contralateral healthy breast. Shown are Group mean and standard deviation of high- and low density results. Results suggest that the increase temporal variance in the tumor bearing breast during baseline can be measured using low density optical sensors.

45 4/30/2014R.L. Barbour

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