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AI / informaticsNeurosurgical review · 2026

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 … Matthew J Shepard

AI in MedicineAI / informatics

Artificial intelligence/machine learning models show high accuracy in predicting high-grade glioma recurrence.

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

Trent Kite et al. · Neurosurgical review · 2026

Background

High-grade gliomas (HGGs) are aggressive tumors with a propensity for recurrence.

Methods

In total, 14 manuscripts encompassing 1,540 patients were selected for systematic review and analysis.

n = 1,540 patients

Results

Artificial intelligence/machine learning models correctly identified 89% of high-grade glioma recurrences and non-recurrences.

n = 1,540 patients

More results

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.

More results

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 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.

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Conclusion

While ongoing validation in larger, prospective databases is needed, preliminary evidence suggests that existing models perform with reasonable sensitivity, specificity, and accuracy.

Post

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

Artificial intelligence/machine learning models show high accuracy in predicting high-grade glioma recurrence.

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