Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

Journal: Journal of neuro-oncology
PMID: 38300389

Abstract

PURPOSE: Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans.

Authors

  • Yonny Hammer
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond. J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel.
  • Wenad Najjar
    Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
  • Lea Kahanov
    Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Yigal Shoshan
    Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel.

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