Background: Despite advances in our understanding of brain cancer, the ability to discern between infiltrated and non-infiltrated tissue with current adjuncts, remains elusive. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mut glioma cells and can be used as a surrogate for tumor infiltration. Further, emerging evidence has shown unique signatures in brain cancer lipidomic profiling, with lipid metabolism playing a key role in brain cancer biology. Here, we aimed to evaluate the impact of desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method to determine infiltration status and the use of DESI-MS to perform intra-operative histological diagnosis. Methods: This is a prospective multi-institution cohort study using intra-operative DESI-MS on freshly obtained tumor samples to evaluate 2-hydroxyglutarate (2HG), with post-hoc validation by review by a board-certified neuropathologist. For tumor cell estimation and tissue diagnosis, high-throughput DESI-MS was used for rapid analysis of high-density arrays using lipidomic profiling. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample. Results: A total of 496 tumor biopsies from 72 patients have been analyzed. Earlier iterations focused on absolute quantitation of 2HG in 247 biopsies from 49 patients to determine IDH genotype yielding sensitivity, specificity, and accuracy values of 89, 100, and 94% for core biopsies (71). Improvement in accuracy was obtained with relative measurement of 2HG in comparison to glutamate allowing for detection of IDH-mut infiltration at the margin, resulting in 93% sensitivity, 100% specificity, and 98% accuracy with turnover times averaging 3 minutes in 183 biopsies from 23 patients. Margin assessment beyond the limits of neuro-navigation and surgeon assessment showed tumor infiltration in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies) with 100% accuracy. Intra-operative tissue diagnosis was pursued in 66 unique tumor samples using DESI-MS lipidomic profiling, including samples of normal brain parenchyma, gliomas, meningiomas and pituitary adenomas, split into two microarray sets (36 and 40 samples). Using estimated principal components as input features, high accuracy ( > 90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Conclusions: We present a novel system for intra-operative evaluation of tumor infiltration at the margin in gliomas and for intra-operative tissue diagnosis of brain cancer. Our method identified tumor infiltration beyond the surgical margins and serves as a foundation for future uses employing DESI-MS to allow for molecularly guided surgery in IDH-mut gliomas.