System development of brain biopsy needle for real-time cancer and blood sensing with spectroscopic approach

Core needle biopsy, in which a surgeon uses a biopsy needle to extract a small fragment of a suspected lesion, is a vital procedure for brain cancer diagnosis and staging tumors. However, the current practice of brain biopsy surgery is inaccurate and risk-prone; it is associated with a negative biopsy rate which leads to repeating the procedure, as well as the risks of rupturing blood vessels during the surgery and causing post-surgical complications. The inaccuracy and safety concerns of the biopsy procedure necessitate a system that can differentiate various types of brain tissue during the surgery so that surgeons can identify the types of tissue in the vicinity of the needle tip before sampling the biopsy. In this project, we explored the possibility of integrating Raman spectroscopy (RS) and diffuse reflectance spectroscopy (DRS) fiber optics system into a clinical biopsy instrument to collect the signals that pertain to light-tissue interaction. The backscattered optical spectra are acquired, processed, analyzed, and classified by a supervised machine learning algorithm in real-time. A guided user interface is written to control the hardware system and output the result of tissue classification on screen simultaneously as the biopsy probe collects data. To this end, we aim to provide intraoperative guidance to surgeons through verifying if the sampling tool is properly located within the lesion, while keeping minimal alteration to the current biopsy practice.