J Neurosurg 143:313–322, 2025
A laser-based device, TumorID, combined with machine learning, rapidly and nondestructively classifies brain tumor tissue intraoperatively. Tested on 46 patients, it distinguished glioma, meningioma, pituitary adenoma, and normal tissue with high accuracy, offering potential to improve neurosurgical decision-making and outcomes
• TumorID is a laser-induced endogenous fluorescence spectroscopy device paired with machine learning for rapid intraoperative brain tumor classification.
• It distinguishes glioma, meningioma, pituitary adenoma, and nonneoplastic tissue in near real time using a 405-nm laser and support vector machine (SVM) algorithm.
• The device requires only 0.5 seconds per scan and does not damage tissue.
• In a study of 46 patients and 761 scans, TumorID achieved a multiclass AUROC of 0.809, demonstrating high classification accuracy.
• Neutral porphyrin emission regions were most significant for tissue differentiation.
• TumorID offers objective, fast, and nondestructive tissue diagnostics, potentially improving surgical decision-making and resection outcomes.
• Future directions include in vivo use, prediction of tumor subtypes and genetics, and integration with other data sources for improved accuracy.
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