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Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.

Venkatesh S Madhugiri1, Dheerendra Prasad2,3,4

  • 1Division of Gamma Knife Radiosurgery, Department of Radiation Medicine, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY, 14203, USA.

Journal of Neuro-Oncology
|October 18, 2024
PubMed
Summary

An artificial intelligence (AI) module shows high accuracy in detecting and measuring brain metastases, especially larger ones. Human expertise remains crucial for smaller lesions near complex brain structures.

Keywords:
Artificial intelligenceBrain metastasesLesion detectionSegmentationVolumetry

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Area of Science:

  • Neuro-oncology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Accurate detection, segmentation, and volumetric analysis of brain lesions are critical in neuro-oncology.
  • Artificial intelligence (AI) models are increasingly used to improve the efficiency of these processes.

Purpose of the Study:

  • To evaluate an AI-based module for detecting and segmenting brain metastases.
  • To compare the AI module's performance against manual detection and segmentation.

Main Methods:

  • Analysis of MRIs from 51 patients treated for brain metastases.
  • Comparison of AI module (Brainlab Smart Brush) performance with manual lesion identification and contouring (gold standard).

Main Results:

  • The AI module achieved 79.2% sensitivity and 95.6% positive predictive value.
  • For lesions >0.1 cc, AI sensitivity was 97.5%, exceeding manual detection (93%).
  • High volumetric agreement (Spearman's ρ = 0.997) between AI and manual segmentation; AI missed smaller lesions near complex anatomy.

Conclusions:

  • The AI module demonstrates high sensitivity for larger brain metastases (>0.1 cc) and strong volumetric accuracy.
  • Human expertise is still essential for detecting smaller lesions and those in complex anatomical regions.
  • AI holds significant potential to enhance efficiency and accuracy in neuro-oncology lesion management.