Breast cancer is a leading cause of death in women, and early accurate diagnosis is crucial for improved survival rates. Current diagnostic methods, including frozen section examination, are time-consuming and may delay treatment. The study proposes that inherent biophysical properties of breast tissues, which change during tumor development, can serve as label-free markers for differentiating tumors from normal tissues. These changes include matrix remodeling, stiffening, collagen deposition, and altered vasculature, impacting electrical, thermal, and mechanical properties. While surgeons currently assess tissue stiffness through palpation, this is subjective and less precise at the margins. Objective measurements of electrical resistivity and thermal conductivity could provide a more accurate assessment, particularly for margin assessment, a significant clinical challenge. Microelectromechanical systems (MEMS) offer a potential solution for the precise measurements required at the millimeter and micrometer scales. Existing MEMS-based technologies for breast cancer diagnosis have largely focused on single-cell or 2D cell culture analysis, with limitations in handling bulk tissue samples. The study aims to address these challenges by developing a system capable of quick and objective assessment of ex vivo breast biopsy tissues.

The literature review section discusses existing methods for breast cancer diagnosis, highlighting the limitations of current techniques like H&E staining and frozen section analysis, especially regarding speed and objectivity in surgical margin assessment. It reviews various MEMS-based technologies applied to breast cancer diagnosis, including microfluidic, microcantilever, and piezoelectric devices, emphasizing their limitations in handling bulk tissue samples. The review highlights the potential of using biophysical properties like electrical resistivity and thermal conductivity for tumor demarcation, noting previous studies demonstrating changes in these properties in cancerous tissues. However, it points out the lack of compact, clinically translatable systems for measuring these properties in ex vivo tissues, motivating the development of the RapidET system.

The RapidET system integrates MEMS-based electrothermal sensors on a microchip with mechatronic actuation and a graphical user interface. The microchip, fabricated on a silicon substrate, includes a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) for sensing. The microchips are integrated onto the platform for easy replacement. The study used paired tumor and adjacent normal breast biopsy samples (N=8 patients) processed in two ways: deparaffinized formalin-fixed paraffin-embedded (FFPE) tissues and formalin-fixed fresh tissues. Bulk resistivity (ρb), surface resistivity (ρs), and thermal conductivity (κ) were measured at 25°C and 37°C using the RapidET system. The microheater heated the tissue, and IDEs measured resistivity, while RTDs measured thermal conductivity. Statistical analysis, including paired and unpaired two-tailed Student's t-tests and Fisher's combined probability test, was used to compare the measured properties between tumor and normal tissues. Linear regression was also performed to evaluate the combined effect of the three parameters.

The study found statistically significant differences in ρb, ρs, and κ between tumor and adjacent normal tissues for both FFPE and formalin-fixed samples. For formalin-fixed samples, the mean ρb for tumors showed a statistically significant 4.42-fold increase from 25°C to 37°C compared to a 3.47-fold increase in adjacent normal tissues (P=0.014). Similar trends were observed for ρs. The mean κ was significantly lower in formalin-fixed tumor tissues (0.309 ± 0.02 W m⁻¹K⁻¹) than in adjacent normal tissues (0.563 ± 0.028 W m⁻¹K⁻¹). Deparaffinized samples showed similar trends, with statistically significant differences in all three parameters between tumor and normal tissues. Combining ρb, ρs, and κ using Fisher's combined probability test and linear regression showed that using all three parameters simultaneously improved the statistical significance of distinguishing tumors from normal tissues.

The findings demonstrate that the RapidET system can effectively differentiate between tumor and normal breast tissues based on their electrical and thermal properties. The higher resistivity in tumor tissues is consistent with previous research and likely reflects changes in the extracellular matrix. The lower thermal conductivity in tumor tissues might be attributed to altered tissue structure and reduced vascularity. The statistically significant differences observed in all three parameters, especially when combined, suggest their potential as robust label-free markers for rapid tumor demarcation. The system's speed and ease of use offer significant advantages over existing methods, potentially accelerating diagnosis and improving surgical margin assessment in both pathology laboratories and operating rooms. The results support the use of combined electrical and thermal measurements for improving the accuracy and speed of breast cancer diagnosis.

The RapidET system provides a rapid, label-free method for distinguishing breast tumors from normal tissues using electrothermal sensing. The statistically significant differences in bulk and surface resistivity and thermal conductivity between tumor and normal tissues demonstrate the system's potential for improving the speed and accuracy of breast cancer diagnosis. Future work could focus on validating the system with a larger patient cohort and investigating its use in other cancer types. Integration with surgical tools could enable real-time intraoperative margin assessment.

The study's sample size (N=8) is relatively small, limiting the generalizability of the results. Further investigation is needed to establish the system's performance across a broader range of tumor types and stages. The study focused on formalin-fixed and FFPE samples, which may have slightly different properties compared to fresh tissues. More research is needed to fully understand the effect of tissue processing on the measured parameters and to optimize the system's performance for fresh tissues.