Logo image
Back
The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review
Journal article   Open access   Peer reviewed

The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review

Trent Kite, Tushar Nayak, Stephen Jaffee, Mokshal Porwal, Cynthia Han, Praveer Vyas, Aswin Sankaranarayanan, Rodney E Wegner, Pulkit Grover and Matthew J Shepard
Neurosurgical review, v 49(1), 489
23 Jul 2026
DOI: https://doi.org/10.1007/s10143-026-04406-7
PMID: 42487088
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1007/s10143-026-04406-7View
Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0

Abstract

Artificial Intelligence Brain Neoplasms - diagnosis Brain Neoplasms - pathology Glioma - diagnosis Glioma - pathology Humans Neoplasm Grading Neoplasm Recurrence, Local - diagnosis Prediction Algorithms Predictive Learning Models Random Forest Machine Learning
High-grade gliomas (HGGs) are aggressive tumors with a propensity for recurrence. Despite standardized therapies, definitive treatment is elusive. Advancements in artificial intelligence (AI) and machine learning (ML) can potentially identify recurrence probabilities and patterns facilitating individualized therapy. A systematic review was conducted in accordance with the PRISMA guidelines. PubMed, ScienceDirect, and Web of Science databases were queried for reports on the use of AI/ML for the prediction of disease recurrence in HGGs. Using a random-effects model and inverse variance weighting a pooled analysis of key performance metrics (sensitivity, specificity, and accuracy) was conducted on the top performing models from each manuscript. In total, 14 manuscripts encompassing 1,540 patients were selected for systematic review and analysis. Across the included studies, 13/14 (92.9%) were retrospective study designs, with 1/14 (7.1%) prospective study design. Among the 1,540 patients, 1,530 (99.3%) and 10 (0.7%) were histologically classified as WHO grade IV and III respectively. Nine studies (9/14, 64.3%) examined patients undergoing GTR following by adjuvant RT, and five studies (5/14, 35.7%) undergoing STR/NTR followed by adjuvant RT. The Random Forest (RF) model was most frequently utilized, with T2-FLAIR sequences most frequently incorporated into model training. The pooled sensitivity, specificity, and accuracy of the models were 81% (95% CI: 73-87; I² = 85.2%), 75% (95% CI: 65-85; I² = 91.9%), and 79% (95% CI: 64-92; I² = 87.8%), respectively. Following sensitivity analyses, the corresponding estimates were 81% (95% CI: 77-84; I² = 0.0%), 84% (95% CI: 79-88; I² = 38.8%), and 89% (95% CI: 83-95; I² = 0.0%), respectively. The development of an AI/ML model to predict tumor recurrence in HGGs has been an emerging area of research over the past decade. While ongoing validation in larger, prospective databases is needed, preliminary evidence suggests that existing models perform with reasonable sensitivity, specificity, and accuracy.

Metrics

4 Record Views

Details

Logo image