Thermo-optic measurements and their inter-dependencies for delineating cancerous breast biopsy tissue from adjacent normal

The histopathological diagnosis of cancer is the current gold standard to differentiate normal from cancerous tissues. We propose a portable platform prototype to characterize the tissue's thermal and optical properties, and their inter-dependencies to potentially aid the pathologist in making an informed decision. The measurements were performed on 10 samples from five subjects, where the cancerous and adjacent normal were extracted from the same patient. It was observed that thermal conductivity (k) and reduced-scattering-coefficient (μ's) for both the cancerous and normal tissues reduced with the rise in tissue temperature. Comparing cancerous and adjacent normal tissue, the difference in k and μ's (at 940 nm) were statistically significant (p = 7.94e-3), while combining k and μ's achieved the highest statistical significance (6.74e-4). These preliminary results promise and support testing on a large number of samples for rapidly differentiating cancerous from adjacent normal tissues

Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer from Adjacent Normal Tissue

OBJECTIVE: While performing surgical excision for breast cancer (lumpectomy), it is important to ensure a clear margin of normal tissue around the cancer to achieve complete resection. The current standard is histopathology; however, it is time-consuming and labour-intensive requiring skilled personnel. METHOD: We describe a Hybrid Spectral-IRDx - a combination of the previously reported Spectral-IRDx tool with multimodal ultrasound and NIR spectroscopy techniques. We show how this portable, cost-effective, minimal-contact tool could provide rapid diagnosis of cancer using formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues. RESULTS: Using this new tool, measurements were performed on cancerous/fibroadenoma and its adjacent normal tissues from the same patients (N=14). The acoustic attenuation coefficient () and reduced scattering coefficient (s) (at 850, 940, and 1060 nm) for the cancerous/fibroadenoma tissues were reported to be higher compared to adjacent normal tissues, a basis of delineation. Comparing FF cancerous and adjacent normal tissue, the difference in s at 850 nm and 940 nm were statistically significant (p=3.17e-2 and 7.94e-3 respectively). The difference in between the cancerous and adjacent normal tissues for DP and FF tissues were also statistically significant (p=2.85e-2 and 7.94e-3 respectively). Combining multimodal parameters and s (at 940 nm) show highest statistical significance (p=6.72e-4) between FF cancerous/fibroadenoma and adjacent normal tissues. CONCLUSION: We show that Hybrid Spectral-IRDx can accurately delineate between cancerous and adjacent normal breast biopsy tissue. SIGNIFICANCE: The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis

Towards a portable platform integrated with multi-spectral non-contact probes for delineating normal and breast cancer tissue based on near-infrared spectroscopy

Currently, the confirmation of diagnosis of breast cancer is made by microscopic examination of an ultra-thin slice of a needle biopsy specimen. This slice is conventionally formalin-fixed and stained with hematoxylin-eosin and visually examined under a light microscope. This process is labor-intensive and requires highly skilled doctors (pathologists). In this paper, we report a novel tool based on near-infrared spectroscopy (Spectral-IRDx) which is a portable, non-contact, and cost-effective system and could provide a rapid and accurate diagnosis of cancer. The Spectral-IRDx tool performs absorption spectroscopy at near-infrared (NIR) wavelengths of 850 nm, 935 nm, and 1060 nm. We measure normalized detected voltage (Vdn) with the tool in 10 deparaffinized breast biopsy tissue samples, 5 of which were cancer (C) and 5 were normal (N) tissues. The difference in Vdn at 935 nm and 1060 nm between cancer and normal tissues is statistically significant with p-values of 0.0038 and 0.0022 respectively. Absorption contrast factor (N/C) of 1.303, 1.551, and 1.45 are observed for 850 nm, 935 nm, and 1060 nm respectively. The volume fraction contrast (N/C) of lipids and collagens are reported as 1.28 and 1.10 respectively. Higher absorption contrast factor (N/C) and volume fraction contrast (N/C) signifies higher concentration of lipids in normal tissues as compared to cancerous tissues, a basis for delineation. These preliminary results support the envisioned concept for non-invasive and non-carcinogenic NIR-based breast cancer diagnostic platform, which will be tested using a larger number of samples

Experimentation and Multiphysical Modeling of Bioanalytical Microdevices

Bioanalytics involves quantitative measurements of complex biological samples that contain metabolites, DNA, RNA, and proteins. Efficient sample preparation for downstream analysis and sensitive detection of analytes can be achieved via bioanalytical microdevices. Fully realizing the potential of these devices requires tool characterization and bioprocess optimization, in addition to understanding device physics. Therefore, this thesis introduces multiphysical modeling and experimentation of microdevices, with applications to diabetes care and single-cell analysis. To understand the physics of viscometric glucose microsensors, this thesis presents a model of the sensor, which couples the fluid flow with vibrating diaphragms. The model is used to predict the sensor response to glucose via theory of squeeze-film damping and vibrations of pre-stressed plate. A first-principle-based model resulting from the theory can be evaluated from the device's geometric and material properties, and quantitatively determines the device response to vibrational excitations at varying glucose concentrations. Next, this thesis introduces a theoretical model for viscometric glucose microsensors that employ harmonic microcantilever oscillation in the sensing liquid. The presented model associates the unsteady Stokes equation with the motion of a bounded viscous liquid to understand the hydrodynamic impact on the cantilever. With a proper consideration of the viscosity and bounded geometry of liquid media, the model relaxes the thin-film assumption required for the diaphragm-based model, enabling an accurate representation of fluid-structure interactions based on fundamental structural vibration and fluid flow equations. Next, this thesis presents an experimental exploration of a hydrogel-based affinity microsensor for glucose monitoring via dielectric measurements. The microsensor incorporates a synthetic hydrogel that is attached to the device surface via in situ polymerization, which eliminates mechanical moving parts required in the viscometric glucose sensors. Changes in the dielectric properties of the hydrogel when binding reversibly with glucose molecules have been measured using a MEMS capacitive transducer to determine the glucose concentration. Experimental results demonstrate that in a glucose concentration range of 0–500 mg/dL and with a resolution of 0.35 mg/dL or better, the microsensor exhibits a repeatable and reversible response, and can potentially be useful for continuous glucose monitoring in diabetes care. Additionally, this thesis presents a microfluidic preprocessing method that integrates single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis at single-cell level for individual cells. The approach utilizes a photocleavable bead-based microfluidic device to synthesize and deliver stable complementary DNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. Finally, this thesis ends with concluding remarks and directions of future work towards continuous glucose monitoring and high-throughput single-cell genetic analysis