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

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 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.