Conventional diagnosis of breast cancer involves the histological and immunohistochemical analysis of tissue sections obtained through clinical biopsy. For surgical margin assessment within the operating room, the standard technique is frozen section examination, which takes between 30 min to 2 hours to give results. Technologies for rapid and label-free diagnosis of malignancies from breast tissues thus have significant potential for application in pathology laboratories and operating rooms.
A team led by Prof. Annapoorni Rangarajan and Prof. Hardik J. Pandya (BEES lab) along with researchers from Assam Medical College, reports the design, development, and clinical validation of a MEMS-based platform (the RapidET system) for characterisation of ex vivo breast biopsy tissues to classify them as tumor or normal. The RapidET system developed at BEES lab integrates silicon microchips with interdigitated electrodes, a microheater, resistance temperature detectors, and onboard electronics for control, actuation, and data acquisition. The microchips can simultaneously measure the electrical and thermal properties of the tissue and its temperature-dependent variations. Measurements performed on deparaffinized and formalin-fixed breast biopsy tissues show a higher surface and bulk electrical resistivity and lower thermal conductivity for tumors compared to the adjacent normal tissues. The study also presents a novel method of using changes in electrical resistivity with temperature and combining it with thermal conductivity measurements as a metric for classifying tissues as tumor or normal while detailing the biochemical and biophysical basis for the same.
This work has been spearheaded by Ph.D. student Anil Vishnu G. K. from the Centre for BioSystems Science and Engineering (BSSE), and funded by SERB (project lead: Prof. Hardik J Pandya).
GK, Anil Vishnu, Gayatri Gogoi, Bhagaban Behera, Saeed Rila, Annapoorni Rangarajan, and Hardik J. Pandya. “RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues.” Microsystems & Nanoengineering 8, no. 1 (2022): 1-16.