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Related Experiment Video

Updated: Sep 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Published on: November 30, 2022

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Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Gleb Danilov1, Vladislav Korolev2, Michael Shifrin1

  • 1Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.

Studies in Health Technology and Informatics
|June 8, 2022
PubMed
Summary

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Deep learning accurately subtypes gliomas using MRI scans, achieving 83% accuracy. This non-invasive approach aids in grading brain tumors, potentially improving patient care and reducing costs.

Area of Science:

  • Neuro-oncology
  • Medical imaging
  • Artificial intelligence

Background:

  • Gliomas are common brain tumors with varied prognoses, classified by WHO grades.
  • Non-invasive diagnostics for nervous system neoplasms are needed to improve care and reduce costs.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I-IV).
  • To assess DL performance on preoperative magnetic resonance imaging (MRI) data.

Main Methods:

  • A pilot study included 707 MRI studies from Burdenko Neurosurgery Center's database.
  • A "3D classification" deep learning approach was utilized.
  • Analysis focused on MR T1 axial images with contrast enhancement.

Main Results:

Keywords:
Deep LearningMagnetic Resonance ImagingTumor Grading

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  • The "3D classification" approach achieved an accuracy of 83% and an ROC AUC of 0.95.
  • Deep learning demonstrated the separability of MR T1 axial images by WHO grade.
  • Results are consistent with other studies using different methodologies.

Conclusions:

  • Deep learning shows promise for non-invasive glioma subtyping based on MRI.
  • This approach could revolutionize glioma diagnosis and patient management.
  • Preliminary results support the feasibility of DL for WHO grading of gliomas.