JMIR Publications

ABSTRACT

Background:

Adult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is essential for maximizing survival prospects. Molecular classification, particularly the detection of IDH mutations and 1p/19q codeletions, has become crucial for accurate diagnosis and prognosis. Artificial Intelligence (AI) has emerged as a promising adjunct in enhancing diagnostic accuracy using histopathological images.

Objective:

This study aims to systematically evaluate the performance of AI models in detecting and classifying IDH mutation status and 1p/19q gene codeletion in adult-type gliomas using histopathological images.

Methods:

A systematic review was conducted in accordance with PRISMA-DTA guidelines. Seven databases (e.g., MEDLINE, Embase, Scopus, etc.) were searched for studies published between 2015 and 2025. Eligible studies employed AI models on histopathological images for molecular classification of adult-type gliomas and reported performance metrics. Study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 tool were conducted independently by two reviewers. Extracted data was synthesized narratively.

Results:

A total of 2453 reports were identified, with 22 studies meeting the inclusion. The pooled average accuracy, sensitivity, specificity, and AUC across studies were 85.46%, 84.55%, 86.03%, and 86.53%, respectively. Hybrid models demonstrated the highest diagnostic performance (accuracy 92.80%, sensitivity 89.62%). In general, AI models that used multi-modal data outperformed those that used uni-modal data in terms of sensitivity (90.15% vs. 84.31%) and AUC (88.93% vs. 86.29%). Furthermore, models had a better overall performance in identifying IDH mutations than 1p/19q codeletions, with higher accuracy (86.13% vs. 81.63%), specificity (86.61% vs. 78.11%), and AUC (86.74% vs. 85.15%). Unexpectedly, AI models designed for binary classification exhibited lower performance than those for multi-class classification in terms of both accuracy (91.98% vs. 84.02%) and sensitivity (93.41% vs. 80.18%).

Conclusions:

AI models show strong potential as complementary tools for the molecular classification of adult-type gliomas using histopathology images, particularly for IDH mutation detection. However, these findings are constrained by the limited number of studies, the focus on adult-type gliomas, lack of meta-analysis, and restriction to English-language publications. While AI offers valuable diagnostic support, it must be integrated with expert clinical judgment. Future research should prioritize larger, more diverse datasets and multimodal AI frameworks, and extend to other brain tumor types for broader applicability.