Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization

Abstract

Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery. Aim: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system. Approach: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1 cm 2. Machine learning classifiers were trained and validated on porcine adipose and muscle tissue. Results: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95 mm 2 in ∼3 min using a support vector machine. Conclusions: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.

Analytical methods used for the characterisation of specific features of biological tissues related with obesity: A review

“…The development of Raman imaging probes for intraoperative cancer surgery guidance, the approach used in Refs. [92,93], had a great potential to improve minimally invasive procedure performance with an endoscope or surgical robots. Both of those technologies are currently using an imaging bundle to recreate white-light images of the tissue from which the surgeon guided himself through the tissue.…”

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

“…Apart from that, it would be a great help for surgery if the tumor border location could be provided to the operator intraoperatively through a handheld device. A handheld macro Raman imaging system was reported to be designed for detecting tissue edge features, and the team concluded that it is necessary to work on a detection system with a larger field of view for tumor margin detection in hand ( 23 ). In this project, they conducted experiments on the fat and muscle tissues of pigs and trained the classifier with the obtained data using machine learning methods, which resulted in an in vitro validation result of 99% accuracy and plotting the boundary probabilities by the phenomenon that the classifier is found to decrease the prediction rate at the junction of fat and muscle tissues.…”

“…Numerous studies have reported the applicability of Raman spectroscopy in the diagnosis of several tumors, which include colorectal cancer ( 16 ), breast cancer ( 17 ), nasopharyngeal carcinoma ( 18 ), skin cancer ( 19 , 20 ), gastric cancer ( 21 ), and prostate cancer ( 22 ), etc. These studies show the prospect of Raman spectroscopy as a novel surgical adjunct to assist in the diagnosis of tumors, and the prominence of Raman spectroscopy in the diagnosis of these tumors and its specific properties - non-invasive, rapid, and accurate - makes it potentially capable of supporting neurosurgeons in the rapid identification of glioma boundaries during surgery ( 8 , 23 , 24 ).…”

Neurosurgical Clinical Trials for Glioblastoma: Current and Future Directions

“…As such, serial tumor sampling around the tumor margin is feasible and can permit rapid intraoperative tumor diagnoses [ 29 , 30 , 31 ]. Similar techniques are also being developed using a hand-held device capable of delineating glioma Raman spectra intraoperatively [ 32 , 33 ]. Overall, these techniques may facilitate the detection of glioma infiltration and, ultimately, improve outcomes for patients by improving the extent of resection.…”