Intraoperative brain tumor classification via laser-induced fluorescence spectroscopy and machine learning.

Objective: To optimize neurosurgical tumor resection, tissue types and borders must be appropriately identified. Authors of this study established the use of a nondestructive laser-based endogenous fluorescence spectroscopy device, "TumorID," to almost immediately classify a specimen as glioma, meningioma, pituitary adenoma, or nonneoplastic tissue in the operating room, utilizing a machine learning algorithm.

Methods: TumorID requires only 0.5 seconds to collect data, without the need for any dyes or tissue manipulation, and utilizes a 100-mW, 405-nm laser that does not damage the tissue. The system was used in the operating room to scan ex vivo specimens from 46 patients (mean age 52 years) with glioma (8 patients), meningioma (10 patients), pituitary adenoma (23 patients), and nonneoplastic tissue resected during an epilepsy operation (5 patients). A support vector machine algorithm was trained to distinguish between these lesions and classify them in near real time. Statistical significance was determined through a generalized estimating equation on the area under the known fluorophore emission regions for free reduced nicotinamide adenine dinucleotide (NADH), bound NADH, flavin adenine dinucleotide, and neutral porphyrins.

Results: Ultimately, the machine learning model showed a high degree of classification power with a multiclass area under the receiver operating characteristic curve of 0.809 ± 0.002. The areas under the curve for neutral porphyrins were found to be statistically significant (p < 0.001) and to have the largest impact on model output.

Conclusions: This initial ex vivo clinical study demonstrated the ability of TumorID to rapidly differentiate and classify various pathologies and surrounding brain in a configuration that can be easily translated to scan in vivo. This classification power could allow TumorID to augment surgical decision-making by enabling rapid intraoperative tissue diagnostics and border delineation, potentially improving patient outcomes by allowing for a more informed and complete resection.