Abstract
Intraoperative identification of the isocitrate dehydrogenase type 1 (IDH1) genotype, a key molecular marker in glioma, is essential for optimizing surgical strategies and tailoring post-surgical treatments. However, current clinical practices lack effective methods for real-time IDH1 genotype detection during surgery. Here, a novel strategy is proposed for intraoperative IDH1 genotype identification by simultaneously measuring two redox-related metabolites. A surface-enhanced Raman scattering (SERS) probe is developed to detect glutathione and hydrogen peroxide concentrations through orthogonally responsive Raman signals. Additionally, a deep learning algorithm is implemented, leveraging 2D Raman spectra transformation and multi-task learning to enhance measurement speed and accuracy. This AI-assisted SERS approach can identify the IDH1 genotype in glioma patients within 7 min. In a cohort of 31 glioma patients, the system achieved an area under the receiver operating characteristic curve of 0.985 for accurate IDH1 genotype differentiation. This study holds significant promise for refining surgical decision-making and personalizing post-surgical treatments by enabling rapid intra-operative molecular biomarker identification.