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Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

Patrick H LuckettMichael O OlufawoKi Yun ParkBidhan LamichhaneDonna DierkerGabriel Trevino VerasteguiJohn J LeePeter YangAlbert KimOmar H ButtMilan G ChhedaAbraham Z SnyderJoshua S ShimonyEric C Leuthardt
Published in: Journal of neuro-oncology (2024)
The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.
Keyphrases
  • resting state
  • machine learning
  • functional connectivity
  • end stage renal disease
  • high grade
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • prognostic factors
  • minimally invasive
  • deep learning