1148 Novel Microsensor Integrated on a Steerable Robot for Real-Time High Grade Glioma Tissue Detection

INTRODUCTION: Quantitatively distinguishing tumor tissue intraoperatively in real-time from normal tissue remains an unsolved challenge in neurosurgical oncology, particularly regarding glioma surgery. While numerous options to improve extent of resection in tumor surgery exist, none are quantitative. Micro-electro-mechanicalsystems (MEMS) sensors (micro sensors) offer the advantage of quantitative sensing of novel micro-forces and can be packaged onto surgical tools used in robotically assisted and/or minimally invasive procedures; MEMS sensors have not been utilized in neurosurgical oncology. METHODS: The sensor microfabrication process, the packaging of the micro-force sensor, the integration of the sensor on the steerable robot, and the design of the steerable robot are described in the study. The proposed sensor is characterized using a commercial force sensor and was found to measure forces up to 0.4 N. RESULTS: The sensor response showed high linearity (∼99.5%) and had a maximum average error of ±18 mN, i.e., 4.5% of thefull-scale output (FSO), indicating reliability and repeatibility of sensor read-out. The sensor’s response upon indentation on paired formalin-fixed-paraffinembedded (FFPE) normal and tumor (GBM) human brain tissue samples obtained from tumor resection surgeries. GBM tissue samples showed a higher stiffness than normal tissue samples and could quantitatively be distinguished between neoplastic and normal tissue. Finally, to test the effect of the robotic motion on the sensor response and reproducibility, the sensor was integrated into a steerable robotic system for comparison and proof of concept. CONCLUSIONS: We have developed novel micro (MEMS) sensors capable of quantitatively distinguishing GBM from adjacent normal tissue in real time. This was tested on clinical tissue samples and has potential for integration into robotic systems for automated tumor resection.