P10.15.B ENHANCING GLIOBLASTOMA TREATMENT STRATEGIES: A MACHINE LEARNING DECISION TREE MODEL FOR PREDICTING PROGRESSION FREE SURVIVAL USING INTEGRATED SURGICAL, VOLUMETRIC, AND MOLECULAR DATA

Satragno, Camilla; Dautun, L; Sambourg, K; Veres, Cristina; Carré, Alexandre; Mrissa, Linda; Yuste, Claudia; Hachemi, T; Savanović, Milovan; Arman Karakaya, Yeliz; Jenny, C.; Çifliku, Vjona; Meyer, P.; Jacob, J.; Maingon, P.; Vakalopoulou, Maria; Bockel, S.; Dhermain, F.; Noël, G.; Deutsch, Éric; Robert, Charlotte

doi:10.1093/neuonc/noae144.191

Public

P10.15.B ENHANCING GLIOBLASTOMA TREATMENT STRATEGIES: A MACHINE LEARNING DECISION TREE MODEL FOR PREDICTING PROGRESSION FREE SURVIVAL USING INTEGRATED SURGICAL, VOLUMETRIC, AND MOLECULAR DATA

Published in Neuro-Oncology • Oct 1, 2024

Authors:

Camilla Satragno

L Dautun

K Sambourg

Abstract

Abstract BACKGROUND Predicting progression-free survival (PFS) in glioblastoma (GB) is crucial for post-surgery treatment decisions. Detecting post-operative volumes via pre-radiotherapy (preRT) MRI, combined with clinical and molecular data, improves prognosis. A decision tree model using these fea...

Subject

Decision tree

Glioblastoma

Tree (set theory)

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