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Related Experiment Video

Updated: Aug 13, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

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Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.

Burak B Ozkara1, Melissa M Chen1, Christian Federau2

  • 1Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.

Cancers
|January 21, 2023
PubMed
Summary

Deep learning models show high effectiveness in detecting brain metastases (BMs) from MRI scans, achieving 89% detectability. Further standardization is needed for accurate false positive rate analysis in future studies.

Keywords:
artificial intelligencebrain metastasisdeep learningmagnetic resonance imagingpooled analysis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Manual detection of brain metastases (BMs) is a time-intensive process.
  • Deep learning (DL) offers a potential solution for automating BM detection.

Purpose of the Study:

  • To systematically review and meta-analyze the performance of DL models using MRI for detecting BMs in cancer patients.
  • To assess the diagnostic accuracy and detectability of DL algorithms in identifying BMs.

Main Methods:

  • Systematic literature search of MEDLINE, EMBASE, and Web of Science until September 2022.
  • Inclusion of original research articles using DL with MRI for BM detection, excluding reviews and machine learning studies.
  • Quality assessment using QUADAS-2 and CAAI methods, with 24 studies included in quantitative analysis.

Main Results:

  • The pooled patient-wise and lesion-wise detectability of BMs using DL models was 89%.
  • Deep learning algorithms demonstrate significant effectiveness in detecting BMs.
  • Pooled analysis of false positive rates was not feasible due to inconsistent reporting across studies.

Conclusions:

  • Deep learning models are effective tools for detecting brain metastases using MRI.
  • Improved adherence to reporting checklists is recommended for future AI in medical imaging studies.
  • Further standardization is needed to enable robust analysis of false positive rates.