Poster S13 - Accounting for the Viscoelastic Properties of Breast Tissue in Optical Coherence Elastography

Introduction: A major characteristic of breast cancer is local stiffening of the tumor and surrounding tissue [1]. This change in elasticity is an important metric in breast cancer detection and tumor removal. Additionally, increased stiffness is associated with worse prognoses , as it can correlate to heighted cancer aggression, reduced drug efficacy, and initiation of metastasis. There is a wide range of studies investigating the relationship between breast cancer and tissue stiffness; yet the data from these studies are highly inconsistent across different characterization methodologies, preventing a comprehensive and consistent understanding of the biomechanical properties of breast cancer. Herein we hypothesize that this discrepancy can be explained, in part, to tissue viscous properties causing elevated modulus measurements when characterized at faster strain rates, and that it can be corrected using a simple viscoelastic model.

Materials and

Methods: Alginate was used to mimic breast tissue because it has similarly high water content and exhibits viscoelastic material behavior [2]. Alginate gels were made at concentrations of 0.5, 0.75, and 1.0%, with 2% CaCl2 crosslinking for 15 minutes. Silicone gels were also created to serve both as a stress-sensor layer for Optical Coherence Elastography (OCE), and as a linearly elastic, non-viscous, control. TiO2 was added as a scattering agent at 1mg/mL prior to gel curing. For each gel formulation, cylindrical, 8-mm samples were mechanically characterized by either compression testing or OCE. Compression testing was performed on a Biomomentum Mach-1 V500cs micromechanical system, and samples were compressed uniaxially 0-20% strain, at three different rates (5, 60, or 1000 μm/s). OCE was performed, as previously described in Dong et al., utilizing a silicone stress-sensor layer placed on top of the sample [3]. A piezoelectric ring actuator cyclically compressed the sample approximately 6 μm , at a frequency of 9.7 Hz, while Optical Coherence Tomography (OCT) imaging was performed in the unloaded and fully-loaded state. OCT to OCE processing was done using a custom MATLAB script As is typical for both compression and OCE, Hooke’s Law (E=σ/ε ) was used to calculate the elastic modulus, assuming a purely elastic response.

Results, Conclusions, and Discussions: Results
A compilation of measured elastic moduli in both healthy and cancerous breast tissue from recent literature (Figure 1) demonstrated distinct differences in measured elastic moduli when compared across characterization methods [4–20]. The variation in moduli was generally greater between the tumor samples than healthy breast tissue. Additionally, although there were some small differences between reported values for uniaxial compression and shear wave elastography, OCE showed consistently elevated values compared to any other methodology. The primary difference, biomechanically speaking, between uniaxial compression and OCE is that OCE characterizes at a faster compression rate and over a much smaller strain range (amplitude). These findings suggest that viscous properties of the tumors are affecting the reported moduli.
Within our silicone and alginate gel experiments, evaluating elastic modulus with respect to strain-normalized strain rate shows that silicone remained relatively stable across the rates, exhibiting an elastic response, and providing comparable values between compression and OCE (Figure 2). However, moduli in all three concentrations of alginate increased exponentially with strain rate/strain indicating: (i) that alginate’s viscosity significantly affected the measured moduli, and (ii) the relationship between viscous component and strain rate/strain appears to be nonlinear (Figure 2). To model this behavior, for each concentration of alginate, the viscous component was fit using a power law, so that the viscoelastic model was E_measured=E_0+a(ε ̇/ε)^b, which agreed well with the experimental data (Figure 2).
Discussion and Conclusion
OCE has many distinct advantages over conventional biomechanical testing for tissue and organoids [3]; including its ability to determine material properties, and their spatial variation, nondestructively. However, for OCE to be successful the measured values must be accurate. Accounting for viscous effects greatly improves elastic modulus accuracy, particularly when characterizing viscoelastic materials at high strain rates. The next step in this research is to characterize the alginate hydrogels over a wide range of strain rates utilizing dynamic mechanical analysis (DMA) and utilize the resulting data to establish a relationship for how the viscous contribution varies with strain rate. This relationship can be used within OCE to accurately report elastic properties of alginate hydrogels and biomechanically similar materials.

Acknowledgements (Optional): Acknowledgements: This study was supported, in part, by NIH R01 BRG CA207725 (DTC/XI), NIH R01 CA233188, and New York State Rowley Breast Cancer Scientific Research Award DOH01-C37904GG-3450000 (DTC). We thank Dr. Brendan Kennedy and his laboratory at the University of Western Australia for assistance with OCE and the MATLAB code.

References:
1. Bordeleau F, Mason BN, Lollis EM, et al. Matrix stiffening promotes a tumor vasculature phenotype. Proc Natl Acad Sci U S A. 2017;114(3):492-497. doi:10.1073/pnas.1613855114
2. Gorroñogoitia I, Urtaza U, Zubiarrain-Laserna A, Alonso-Varona A, Zaldua AM. A Study of the Printability of Alginate-Based Bioinks by 3D Bioprinting for Articular Cartilage Tissue Engineering. Polymers. 2022;14(2). doi:10.3390/polym14020354
3. Dong L, Wijesinghe P, Sampson DD, Kennedy BF, Munro PRT, Oberai AA. Volumetric quantitative optical coherence elastography with an iterative inversion method. Biomed Opt Express. 2019;10(2):384. doi:10.1364/boe.10.000384
4. Acerbi I, Cassereau L, Dean I, et al. Human Breast Cancer Invasion and Aggression Correlates with ECM Stiffening and Immune Cell Infiltration. Integr Biol Quant Biosci Nano Macro. 2015;7(10):1120-1134. doi:10.1039/c5ib00040h
5. Plodinec M, Loparic M, Monnier CA, et al. The nanomechanical signature of breast cancer. Nat Nanotechnol. 2012;7(11):757-765. doi:10.1038/nnano.2012.167
6. Samani A, Zubovits J, Plewes D. Elastic moduli of normal and pathological human breast tissues: An inversion-technique-based investigation of 169 samples. Phys Med Biol. 2007;52(6):1565-1576. doi:10.1088/0031-9155/52/6/002
7. Prokop J, Maršálek P, Sengul I, et al. Evaluation of breast stiffness pathology based on breast compression during mammography: Proposal for novel breast stiffness scale classification. Clinics. 2022;77:100100. doi:10.1016/j.clinsp.2022.100100
8. Gubarkova EV, Sovetsky AA, Zaitsev VY, et al. OCT-elastography-based optical biopsy for breast cancer delineation and express assessment of morphological/molecular subtypes. Biomed Opt Express. 2019;10(5):2244. doi:10.1364/boe.10.002244
9. Gubarkova EV, Sovetsky AA, Matveev LA, et al. Nonlinear Elasticity Assessment with Optical Coherence Elastography for High-Selectivity Differentiation of Breast Cancer Tissues. Materials. 2022;15(9):3308. doi:10.3390/ma15093308
10. Plekhanov AA, Potapov AL, Pavlov MV, et al. Side-by-Side OCE-Study of Elasticity and SHG-Characterization of Collagen Fibers in Breast Cancer Tissue before and after Chemotherapy. J Biomed Photonics Eng. 2023;9(2):020305.
11. Fang Q, Frewer L, Zilkens R, et al. Handheld volumetric manual compression-based quantitative microelastography. J Biophotonics. 2020;13(6):e201960196. doi:10.1002/jbio.201960196
12. Wang S, Larin KV. Optical coherence elastography for tissue characterization: a review. J Biophotonics. 2015;8(4):279-302. doi:10.1002/jbio.201400108
13. Denis M, Gregory A, Bayat M, et al. Correlating Tumor Stiffness with Immunohistochemical Subtypes of Breast Cancers: Prognostic Value of Comb-Push Ultrasound Shear Elastography for Differentiating Luminal Subtypes. PLoS ONE. 2016;11(10):e0165003. doi:10.1371/journal.pone.0165003
14. Bae JS, Chang JM, Lee SH, Shin SU, Moon WK. Prediction of invasive breast cancer using shear-wave elastography in patients with biopsy-confirmed ductal carcinoma in situ. Eur Radiol. 2017;27(1):7-15. doi:10.1007/s00330-016-4359-6
15. Papageorgiou I, Valous NA, Hadjidemetriou S, Teichgräber U, Malich A. Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms. Diagnostics. 2022;12(12):3140. doi:10.3390/diagnostics12123140
16. Evans A, Whelehan P, Thomson K, et al. Quantitative shear wave ultrasound elastography: initial experience in solid breast masses. Breast Cancer Res BCR. 2010;12(6):R104. doi:10.1186/bcr2787
17. Kim JY, Kim JJ, Hwangbo L, et al. Tumor stiffness measured by shear-wave elastography: association with disease-free survival in women with early-stage breast cancer. Br J Radiol. 2021;94(1128):20210584. doi:10.1259/bjr.20210584
18. Sim YT, Vinnicombe S, Whelehan P, Thomson K, Evans A. Value of shear-wave elastography in the diagnosis of symptomatic invasive lobular breast cancer. Clin Radiol. 2015;70(6):604-609. doi:10.1016/j.crad.2015.02.004
19. Wang ZL, Li JL, Li M, Huang Y, Wan WB, Tang J. Study of quantitative elastography with supersonic shear imaging in the diagnosis of breast tumours. Radiol Med (Torino). 2013;118(4):583-590. doi:10.1007/s11547-012-0903-x
20. Athanasiou A, Tardivon A, Tanter M, et al. Breast Lesions: Quantitative Elastography with Supersonic Shear Imaging—Preliminary Results. Radiology. 2010;256(1):297-303. doi:10.1148/radiol.10090385