AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.
Authors
Affiliations (6)
Affiliations (6)
- Center for Image-Guided Neurosurgery, Neurological Surgery Department, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
- Computational Neurosurgery Research Group (CIGNS-CRG), Center for Image-Guided Neurosurgery, Neurological Surgery Department, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
- Department of Neurosurgery, Tufts Medical Center, Boston, MA, USA.
- Department of Neurosurgery, Tufts Medical Center, Boston, MA, USA. [email protected].
- Center for Image-Guided Neurosurgery, Neurological Surgery Department, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA. [email protected].
- Computational Neurosurgery Research Group (CIGNS-CRG), Center for Image-Guided Neurosurgery, Neurological Surgery Department, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA. [email protected].
Abstract
Glioblastoma (GBM) remains one of the most lethal adult primary brain tumors, and neurosurgical decision-making increasingly depends on integrating imaging, molecular, perioperative, and post-treatment data. Artificial intelligence (AI) methods have been proposed for several clinically relevant GBM tasks, but the literature remains heterogeneous and difficult to translate into practice. We performed a PROSPERO-registered systematic review of AI, machine learning, and deep learning studies using MRI-derived and/or multimodal perioperative data in GBM for prognosis, risk stratification, treatment-response assessment, post-treatment classification, recurrence/progression prediction, and molecular prediction. Risk of bias was assessed using PROBAST-informed criteria. Thirty studies were included. Survival-focused tasks predominated (20/30, 66.7%), with radiomics plus conventional machine learning as the most common model family (13/30, 43.3%), followed by deep learning (8/30, 26.7%) and hybrid deep learning plus radiomics approaches (4/30, 13.3%). Validation was predominantly internal, and external validation was uncommon (5/30, 16.7%). AI shows promise for prognosis and treatment stratification in GBM neurosurgery, but current evidence is limited by heterogeneity, incomplete external validation, and inconsistent methodological reporting. Not applicable.