Simplifying the assessment of human breast cancer by mapping a micro-scale heterogeneity index in optical coherence elastography

Viscoelasticity quantification of cancerous tongue using intraoral optical coherence elastography: a preliminary study

Quantifying the biomechanical properties of the tongue is significant for early diagnosis of tongue carcinoma. Therefore, an intraoral optical coherence elastography system based on a miniature probe was proposed here to evaluate the viscoelasticity of in vivo tongue for the first time. Results of experiments with Sprague-Dawley rats indicate that considerable elasticity diversity occurred between cancerous and normal tongues, and the corresponding ratio of their Young's modulus was evaluated to be 3.74. It is also found that, viscosity in diseased tissue is smaller than that in normal tissue. Additionally, healthy, transitional and cancerous regions in the cancerous tongue can be distinguished easily by calculating viscoelasticity characteristics. Based on this preliminary attempt, our method with advantages of noninvasive, high-resolution, high-sensitivity and real-time detection and convenient operation may have good potential to become a useful tool for tongue carcinoma assessment after further optimization.

Speckle rheological spectroscopy reveals wideband viscoelastic spectra of biological tissues

Mechanical transformation of tissue is not merely a symptom but a decisive driver in pathological processes. Comprising intricate network of cells, fibrillar proteins, and interstitial fluid, tissues exhibit distinct solid-(elastic) and liquid-like (viscous) behaviours that span a wide band of frequencies. Yet, characterization of wideband viscoelastic behaviour in whole tissue has not been investigated, leaving a vast knowledge gap in the higher frequency range that is linked to fundamental intracellular processes and microstructural dynamics. Here, we present wideband Speckle rHEologicAl spectRoScopy (SHEARS) to address this need. We demonstrate, for the first time, analysis of frequency-dependent elastic and viscous moduli up to the sub-MHz regime in biomimetic scaffolds and tissue specimens of blood clots, breast tumours, and bone. By capturing previously inaccessible viscoelastic behaviour across the wide frequency spectrum, our approach provides distinct and comprehensive mechanical signatures of tissues that may provide new mechanobiological insights and inform novel disease prognostication.

Nonlinear Elasticity Assessment with Optical Coherence Elastography for High-Selectivity Differentiation of Breast Cancer Tissues

Soft biological tissues, breast cancer tissues in particular, often manifest pronounced nonlinear elasticity, i.e., strong dependence of their Young’s modulus on the applied stress. We showed that compression optical coherence elastography (C-OCE) is a promising tool enabling the evaluation of nonlinear properties in addition to the conventionally discussed Young’s modulus in order to improve diagnostic accuracy of elastographic examination of tumorous tissues. The aim of this study was to reveal and quantify variations in stiffness for various breast tissue components depending on the applied pressure. We discussed nonlinear elastic properties of different breast cancer samples excised from 50 patients during breast-conserving surgery. Significant differences were found among various subtypes of tumorous and nontumorous breast tissues in terms of the initial Young’s modulus (estimated for stress < 1 kPa) and the nonlinearity parameter determining the rate of stiffness increase with increasing stress. However, Young’s modulus alone or the nonlinearity parameter alone may be insufficient to differentiate some malignant breast tissue subtypes from benign. For instance, benign fibrous stroma and fibrous stroma with isolated individual cancer cells or small agglomerates of cancer cells do not yet exhibit significant difference in the Young’s modulus. Nevertheless, they can be clearly singled out by their nonlinearity parameter, which is the main novelty of the proposed OCE-based discrimination of various breast tissue subtypes. This ability of OCE is very important for finding a clean resection boundary. Overall, morphological segmentation of OCE images accounting for both linear and nonlinear elastic parameters strongly enhances the correspondence with the histological slices and radically improves the diagnostic possibilities of C-OCE for a reliable clinical outcome.

Data-driven modeling of heterogeneous viscoelastic biofilms

Biofilms are typically heterogeneous in morphology, structure, and composition, resulting in non-uniform mechanical properties. The distribution of mechanical properties, in turn, determines the biofilm behavior, such as deformation and detachment. Most biofilm models neglect biofilm heterogeneity, especially at the microscale. In this study, an image-based modeling approach was developed to transform two-dimensional optical coherence tomography (OCT) biofilm images to a pixel-scale non-Newtonian viscosity map of the biofilm. The map was calibrated using the bulk viscosity data from rheometer tests, based on assumed maximum and minimum viscosities and a relationship between OCT image intensity signals and non-Newtonian viscosity. While not quantitatively measuring biofilm viscosity for each pixel, it allows a rational spatial allocation of viscosities within the biofilm: areas with lower cell density, e.g., voids, are assigned lower viscosities, and areas with high cell densities are assigned higher viscosities. The spatial distribution of non-Newtonian viscosity was applied in an established Oldroyd-B constitutive model and implemented using the phase-field continuum approach for the deformation and stress analysis. The heterogeneous model was able to predict deformations more accurately than a homogenous one. Stress distribution in the heterogeneous biofilm displayed better characteristics than that in the homogeneous one, because it is highly dependent on the viscosity distribution. This work, using a pixel-scale, image-based approach to map the mechanical heterogeneity of biofilms for computational deformation and stress analysis, provides a novel modeling approach that allows the consideration of biofilm structural and mechanical heterogeneity. Future research should better characterize the relationship between OCT signal and viscosity, and consider other constitutive models for biofilm mechanical behavior. This article is protected by copyright. All rights reserved.

Technological perspectives on laser speckle micro-rheology for cancer mechanobiology research

SIGNIFICANCE

The ability to measure the micro-mechanical properties of biological tissues and biomaterials is crucial for numerous fields of cancer research, including tumor mechanobiology, tumor-targeting drug delivery, and therapeutic development.

AIM

Our goal is to provide a renewed perspective on the mainstream techniques used for micro-mechanical evaluation of biological tissues and biomimetic scaffoldings. We specifically focus on portraying the outlook of laser speckle micro-rheology (LSM), a technology that quantifies the mechanical properties of biomaterials and tissues in a rapid, non-contact manner.

APPROACH

First, we briefly explain the motivation and significance of evaluating the tissue micro-mechanics in various fields of basic and translational cancer research and introduce the key concepts and quantitative metrics used to explain the mechanical properties of tissue. This is followed by reviewing the general active and passive themes of measuring micro-mechanics. Next, we focus on LSM and elaborate on the theoretical grounds and working principles of this technique. Then, the perspective for measuring the micro-mechanical properties via LSM is outlined. Finally, we draw an overview picture of LSM in cancer mechanobiology research.

RESULTS

With the continued emergence of new approaches for measuring the mechanical attributes of biological tissues, the field of micro-mechanical imaging is at its boom. As one of these competent innovations, LSM presents a tremendous potential for both technical maturation and prospective applications in cancer biomechanics and mechanobiology research.

CONCLUSION

By elaborating the current viewpoint of LSM, we expect to accelerate the expansion of this approach to new territories in both technological domains and applied fields. This renewed perspective on LSM may also serve as a road map for other micro-mechanical measurement concepts to be applied for answering mechanobiological questions.

Strain and elasticity imaging in compression optical coherence elastography: The two-decade perspective and recent advances

Quantitative mapping of deformation and elasticity in optical coherence tomography has attracted much attention of researchers during the last two decades. However, despite intense effort it took ~15 years to demonstrate optical coherence elastography (OCE) as a practically useful technique. Similarly to medical ultrasound, where elastography was first realized using the quasi-static compression principle and later shear-wave-based systems were developed, in OCE these two approaches also developed in parallel. However, although the compression OCE (C-OCE) was proposed historically earlier in the seminal paper by J. Schmitt in 1998, breakthroughs in quantitative mapping of genuine local strains and the Young's modulus in C-OCE have been reported only recently and have not yet obtained sufficient attention in reviews. In this overview, we focus on underlying principles of C-OCE; discuss various practical challenges in its realization and present examples of biomedical applications of C-OCE. The figure demonstrates OCE-visualization of complex transient strains in a corneal sample heated by an infrared laser beam.

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

We present a non-invasive (albeit contact) method based on Optical Coherence Elastography (OCE) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. The method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young’s modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic "stiffness spectra") are initially determined by careful comparison of the "gold-standard" histological data and the OCE-based stiffness map for the corresponding tissue regions. After such pre-calibration, the results of morphological segmentation of OCE-images demonstrate a striking similarity with the histological results in terms of percentage of the segmented zones. To validate the sensitivity of the OCE-method and demonstrate its high correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two antitumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (1) viable, (2) dystrophic, (3) necrotic tumor cells and (4) edema zones very similar to morphological segmentation of histological images. Numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen.

Spatial coordinate corrected motion tracking for optical coherence elastography

We investigate a spatial coordinate correction (SCC) method to track motion with high accuracy for optical coherence elastography (OCE). Through SCC, we refer the displacement field tracked by optical coherence tomography (OCT) in the loaded sample to individual material points defined in a fixed coordinate system. SCC allows OCE to perform spatially and temporally unambiguous tracking of displacement and enables accurate mechanical characterization of biological tissue for cancer diagnosis and tumor margin assessment. In this study, we validated the effectiveness of motion tracking based on SCC using experimental OCE data obtained from ex vivo human breast tissues.

OCT-elastography-based optical biopsy for breast cancer delineation and express assessment of morphological/molecular subtypes

Application of compressional optical coherence elastography (OCE) for delineation of tumor and peri-tumoral tissue with simultaneous assessment of morphological/molecular subtypes of breast cancer is reported. The approach is based on the ability of OCE to quantitatively visualize stiffness of studied samples and then to perform a kind of OCE-based biopsy by analyzing elastographic B-scans that have sizes ~several millimeters similarly to bioptates used for "gold-standard" histological examinations. The method relies on identification of several main tissue constituents differing in their stiffness in the OCE scans. Initially the specific stiffness ranges for the analyzed tissue components (adipose tissue, fibrous and hyalinized tumor stroma, lymphocytic infiltrate and agglomerates of tumor cells) are determined via comparison of OCE and morphological/molecular data. Then assessment of non-tumor/tumor regions and tumor subtypes is made based on percentage of pixels with different characteristic stiffness ("stiffness spectrum") in the OCE image, also taking into account spatial localization of different-stiffness regions. Examples of high contrast among benign (or non-invasive) and several subtypes of invasive breast tumors in terms of their stiffness spectra are given.

Photoacoustic elastography imaging: a review

Elastography imaging is a promising tool-in both research and clinical settings-for diagnosis, staging, and therapeutic treatments of various life-threatening diseases (including brain tumors, breast cancers, prostate cancers, and Alzheimer's disease). Large variation in the physical (elastic) properties of tissue, from normal to diseased stages, enables highly sensitive characterization of pathophysiological states of the diseases. On the other hand, over the last decade or so, photoacoustic (PA) imaging-an imaging modality that combines the advantageous features of two separate imaging modalities, i.e., high spatial resolution and high contrast obtainable, respectively, from ultrasound- and optical-based modalities-has been emerging and widely studied. Recently, recovery of elastic properties of soft biological tissues-in addition to prior reported recovery of vital tissue physiological information (Hb, HbO2, SO, and total Hb), noninvasively and nondestructively, with unprecedented spatial resolution (μm) at penetration depth (cm)-has been reported. Studies demonstrating that combined recovery of mechanical tissue properties and physiological information-by a single (PA) imaging unit-pave a promising platform in clinical diagnosis and therapeutic treatments. We offer a comprehensive review of PA imaging technology, focusing on recent advances in relation to elastography. Our review draws out technological challenges pertaining to PA elastography (PAE) imaging, and viable approaches. Currently, PAE imaging is in the nurture stage of its development, where the technology is limited to qualitative study. The prevailing challenges (specifically, quantitative measurements) may be addressed in a similar way by which ultrasound elastography and optical coherence elastography were accredited for quantitative measurements.

Compression optical coherence elastography with two-dimensional displacement measurement and local deformation visualization

Current compression-based optical coherence elastography (OCE) only measures the axial displacement of a tissue, although the tissue also undergoes lateral displacement and microstructural alteration by the compression. In this Letter, we demonstrate a new compression-based OCE method that visualizes not only axial displacement, but also lateral displacement and microstructural decorrelation (MSD). This method employs complex correlation-based displacement and MSD measurements. It is implemented in a swept-source optical coherence tomography system with an active submicrometer compression. The performance of the method was demonstrated by measuring the porcine carotid artery and esophagus. The results showed significant axial and lateral displacements in the tissues by compression. An MSD map demonstrates high-contrast mechanical-property imaging.

Clinical feasibility of optical coherence micro-elastography for imaging tumor margins in breast-conserving surgery

It has been demonstrated that optical coherence micro-elastography (OCME) provides additional contrast of tumor compared to optical coherence tomography (OCT) alone. Previous studies, however, have predominantly been performed on mastectomy specimens. Such specimens typically differ substantially in composition and geometry from the more clinically relevant wide-local excision (WLE) specimens excised during breast-conserving surgery. As a result, it remains unclear if the mechanical contrast observed is maintained in WLE specimens. In this manuscript, we begin to address this issue by performing a feasibility study of OCME on 17 freshly excised, intact WLE specimens. In addition, we present two developments required to sustain the progression of OCME towards intraoperative deployment. First, to enable the rapid visualization of en face images required for intraoperative assessment, we describe an automated segmentation algorithm to fuse en face micro-elastograms with OCT images to provide dual contrast images. Secondly, to validate contrast in micro-elastograms, we present a method that enables co-registration of en face images with histology of WLE specimens, sectioned in the orthogonal plane, without any modification to the standard clinical workflow. We present a summary of the observations across the 17 specimens imaged in addition to representative micro-elastograms and OCT images demonstrating contrast in a number of tumor margins, including those involved by invasive ductal carcinoma, mucinous carcinoma, and solid-papillary carcinoma. The results presented here demonstrate the potential of OCME for imaging tumor margins.

Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks

Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced function norm regularization that attempts to directly control the function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% - a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.

Wide-field quantitative micro-elastography of human breast tissue

Currently, 20-30% of patients undergoing breast-conserving surgery require a second surgery due to insufficient surgical margins in the initial procedure. We have developed a wide-field quantitative micro-elastography system for the assessment of tumor margins. In this technique, we map tissue elasticity over a field-of-view of ~46 × 46 mm. We performed wide-field quantitative micro-elastography on thirteen specimens of freshly excised tissue acquired from patients undergoing a mastectomy. We present wide-field optical coherence tomography (OCT) images, qualitative (strain) micro-elastograms and quantitative (elasticity) micro-elastograms, acquired in 10 minutes. We demonstrate that wide-field quantitative micro-elastography can extend the range of tumors visible using OCT-based elastography by providing contrast not present in either OCT or qualitative micro-elastography and, in addition, can reduce imaging artifacts caused by a lack of contact between tissue and the imaging window. Also, we describe how the combined evaluation of OCT, qualitative micro-elastograms and quantitative micro-elastograms can improve the visualization of tumor.

Biomedical device innovation methodology: applications in biophotonics

The process of medical device innovation involves an iterative method that focuses on designing innovative, device-oriented solutions that address unmet clinical needs. This process has been applied to the field of biophotonics with many notable successes. Device innovation begins with identifying an unmet clinical need and evaluating this need through a variety of lenses, including currently existing solutions for the need, stakeholders who are interested in the need, and the market that will support an innovative solution. Only once the clinical need is understood in detail can the invention process begin. The ideation phase often involves multiple levels of brainstorming and prototyping with the aim of addressing technical and clinical questions early and in a cost-efficient manner. Once potential solutions are found, they are tested against a number of known translational factors, including intellectual property, regulatory, and reimbursement landscapes. Only when the solution matches the clinical need, the next phase of building a "to market" strategy should begin. Most aspects of the innovation process can be conducted relatively quickly and without significant capital expense. This white paper focuses on key points of the medical device innovation method and how the field of biophotonics has been applied within this framework to generate clinical and commercial success.