Artificial Intelligent-Enhanced Metabolite Profiling for Intraoperative IDH1 Genotyping in Glioma Using an Orthogonally Responsive SERS Probe.

in Advanced science (Weinheim, Baden-Wurttemberg, Germany) by Hang Yin, Xin Zhang, Zheng Zhao, Chong Cao, Minhua Xu, Suhongrui Zhou, Tian Xuan, Ziyi Jin, Limei Han, Yang Fan, Cong Wang, Xiao Zhu, Ying Mao, Jinhua Yu, Cong Li

TLDR

  • A novel AI-assisted SERS approach enables rapid intra-operative IDH1 genotype identification in glioma patients, promising to refine surgical decision-making and personalize post-surgical treatments.

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.

Overview

  • The study proposes a novel strategy for real-time IDH1 genotype identification in glioma patients during surgery.
  • The method uses a SERS probe that detects glutathione and hydrogen peroxide concentrations through Raman signals.
  • A deep learning algorithm is implemented to enhance measurement speed and accuracy.

Comparative Analysis & Findings

  • The system achieved an area under the receiver operating characteristic curve of 0.985 for accurate IDH1 genotype differentiation.
  • The IDH1 genotype was identified in glioma patients within 7 minutes.
  • The method demonstrated high accuracy and speed, promising to refine surgical decision-making and personalize post-surgical treatments.

Implications and Future Directions

  • This study holds significant promise for refining surgical decision-making and personalizing post-surgical treatments in glioma patients.
  • Future research could focus on validating the method in larger cohorts and exploring its potential applications in other types of cancer.
  • The integration of this technology with other intraoperative monitoring tools could lead to further advancements in personalized surgery
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