Design and Development of a Semi-automated System for Electro-thermo-mechanical (Etm) Phenotyping of Breast Tissues Ex-vivo

Abstract

Breast cancer currently accounts for 25% of all cancers diagnosed in women globally. The conventional confirmatory diagnosis of breast cancer involves histological analysis using hematoxylin and eosin (H&E) staining, followed by immunohistochemical analysis for breast cancer biomarkers. For surgical margin assessment within the operating room (OR), the standard technique is frozen section examination. However, this process involves sending the biopsy tissue out of the OR and into the pathology laboratories, with the analysis time for each sample ranging between 30 min and 2 h. This adds to the time of diagnosis as well as the surgery. Therefore, tools and technologies that can provide a rapid and label-free assessment of breast biopsies to delineate between benign, malignant, and adjacent normal tissue can be a significant adjunct to routine diagnostics. This thesis proposes a label-free multimodal approach for breast tumor delineation using electro-thermo-mechanical (ETM) phenotyping with a MEMS-based semi-automated system. First, the design and development of the RapidET system, the MEMS-based semi-automated platform integrated with microchips and electronic modules for biophysical characterization of tissues, is presented. Microchips for electro-thermal characterization of the samples were first integrated with the system. The microchips, fabricated on a silicon substrate, incorporate a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The measurement accuracy of the system was first validated on a murine xenograft tumor model through electro-thermal characterization of ex vivo tumors and healthy tissues. Next, the bulk resistivity (ρB), surface resistivity (ρS), and thermal conductivity (k) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 subjects were measured. The bulk and surface resistivity of tumors showed a significant increase with temperature compared to the adjacent normal. The tumor tissues were also observed to have a significantly lower (0.309 ± 0.02 Wm-1K-1) thermal conductivity than normal (0.563 ± 0.028 Wm-1K-1). Next, the scaled increase in resistivity with temperature observed for the tumor tissues was further explored by performing a bimodal temperature and frequency-dependent electrical transport characterization of the samples (N = 10). Temperature-dependent direct current (DC) transport was modeled under the realm of general effective medium theory. Critical temperature (Tc) as a model fit parameter was found to be higher for adjacent normal (42.8 ± 2.0 ºC) compared to the tumor (36.5 ± 0.9 ºC), indicating an early transition from conducting to the insulating regime in tumor tissues. Frequency-dependent alternating current (AC) transport was observed to follow the scaling law, which is used to model disordered systems and growth in biological systems. The parameter onset frequency fc was higher for adjacent normal (1.1 ± 0.37 MHz) than the tumor (33.5 ± 14.9 kHz), indicating higher disorder in tumor samples. The utility of the model fit parameters in classifying samples as tumor and normal was demonstrated using a support vector machine (SVM) classifier, which showed 91.7% accuracy when compared to 70% as obtained for the raw data. Finally, the system was augmented with a microforce sensor to create a system-of-biochips (SoB) to perform electro-thermo-mechanical phenotyping. The SoB measures the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. Multimodal ETM characterization performed on formalin-fixed breast biopsy samples from N = 14 subjects was able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a gaussian process classifier. After validation with fresh tissues, this multimodal methodology could potentially be used for delineating benign, malignant, and adjacent normal breast tissues during surgery and in pathology labs.