Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

Simple Summary Despite their prevalence in research, ML tools that can predict glioma grade from medical images have yet to be incorporated clinically. The reporting quality of ML glioma grade prediction studies is below 50% according to TRIPOD-limiting model reproducibility and, thus, clinical translation-however, current efforts to create ML-specific reporting guidelines and risk of bias tools may help address this. Several additional deficiencies in the areas of ML model data and glioma classification hamper widespread clinical use, but promising efforts to overcome current challenges and encourage implementation are on the horizon. Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Automated summary

Despite their prevalence in research, ML tools that can predict glioma grade from medical images have not been incorporated clinically. The reporting quality of ML glioma grade prediction studies is low, but current efforts to create reporting guidelines and risk of bias tools may help address this. There are also other deficiencies in ML model data and glioma classification, but promising efforts are being made to overcome these challenges and encourage implementation in clinical settings.