A machine learning-assisted systematic review of preclinical glioma modeling: Is practice changing with the times?

Abstract

Background: Despite improvements in our understanding of glioblastoma pathophysiology, there have been no major improvements in treatment in recent years. Animal models are a vital tool for investigating cancer biology and its treatment, but have known limitations. There have been advances in glioblastoma modeling techniques in this century although it is unclear to what extent they have been adopted.

Methods: We searched Pubmed and EMBASE using terms designed to identify all publications reporting an animal glioma experiment, using a machine learning algorithm to assist with screening. We reviewed the full text of a sample of 1000 articles and then used the findings to inform a screen of all included abstracts to appraise the modeling applications across the entire dataset.

Results: The search identified 26 201 publications of which 13 783 were included at screening. The automated screening had high sensitivity but limited specificity. We observed a dominance of traditional cell line paradigms and the emergence of advanced tumor model systems eclipsed by a large increase in the volume of cell line experiments. Few studies used more than 1 model in vivo and most publications did not verify critical genetic features.

Conclusions: Advanced models have clear advantages in terms of tumor and disease recapitulation and have largely not replaced traditional cell lines which have a number of critical deficiencies that limit their viability in modern animal research. The judicious use of advanced models or more relevant cell lines might improve the translational relevance of future animal glioblastoma experimentation.