Cancer tissue is frequently impossible to distinguish from normal brain during surgery. Gliomas are a class of brain cancer which invade into the normal brain. If left unresected, these invasive cancer cells are the source of glioma recurrence. Moreover, these invasion areas do not show up on standard-of-care pre-operative Magnetic Resonance Imaging (MRI). This inability to fully visualize invasive brain cancers results in subtotal surgical resections, negatively impacting patient survival. To address this issue, we have demonstrated the efficacy of single-point in vivo Raman spectroscopy using a contact hand-held fiber optic probe for rapid detection of cancer invasion in 8 patients with low and high grade gliomas. Using a supervised machine learning algorithm to analyze the Raman spectra obtained in vivo, we were able to distinguish normal brain from the presence of cancer cells with sensitivity and specificity greater than 90%. Moreover, by correlating these results with pre-operative MRI we demonstrate the ability to detect low density cancer invasion up to 1.5cm beyond the cancer extent visible using MRI. This represents the potential for significant improvements in progression-free and overall patient survival, by identifying previously undetectable residual cancer cell populations and preventing the resection of normal brain tissue. While the importance of maximizing the volume of tumor resection is important for all grades of gliomas, the impact for low grade gliomas can be dramatic because surgery can even be curative. This convenient technology can rapidly classify cancer invasion in real-time, making it ideal for intraoperative use in brain tumor resection.