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Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Todd Hollon1,2, Cheng Jiang3,4, Asadur Chowdury3

  • 1Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA. tocho@med.umich.edu.

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Summary

DeepGlioma rapidly screens diffuse gliomas using artificial intelligence and optical imaging. This AI tool accurately predicts molecular alterations, improving brain tumor diagnosis and treatment planning.

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Area of Science:

  • Neuro-oncology
  • Computational pathology
  • Medical artificial intelligence

Background:

  • Molecular classification is crucial for brain tumor management, enabling accurate prognostication and personalized treatment.
  • Current molecular diagnostic testing is often slow, hindering timely surgical and adjuvant treatment decisions and clinical trial enrollment for diffuse gliomas.

Purpose of the Study:

  • To develop and validate DeepGlioma, a rapid AI-based diagnostic screening system to streamline molecular diagnosis of diffuse gliomas.
  • To assess the accuracy of DeepGlioma in predicting key molecular alterations for glioma classification.

Main Methods:

  • DeepGlioma was developed using a multimodal dataset combining stimulated Raman histology (SRH) and public genomic data.
  • SRH is a rapid, label-free, optical imaging technique.
  • The system was prospectively tested on 153 patients with diffuse glioma using real-time SRH imaging.

Main Results:

  • DeepGlioma achieved a mean molecular classification accuracy of 93.3% ± 1.6% in predicting IDH mutation, 1p19q co-deletion, and ATRX mutation.
  • The system provided real-time molecular screening in less than 90 seconds.
  • The AI model demonstrated high accuracy in a multicenter, international cohort.

Conclusions:

  • Artificial intelligence combined with optical histology offers a rapid and scalable adjunct to traditional methods for molecular screening of diffuse gliomas.
  • DeepGlioma has the potential to significantly improve the efficiency of brain tumor diagnosis and treatment planning.
  • This approach can facilitate faster clinical decision-making and potentially increase enrollment in clinical trials.