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

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 specif...

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Bibliographic Details
Published in:Cancers 2022-05, Vol.14 (11), p.2623
Main Authors: Merkaj, Sara, Bahar, Ryan C, Zeevi, Tal, Lin, MingDe, Ikuta, Ichiro, Bousabarah, Khaled, Cassinelli Petersen, Gabriel I, Staib, Lawrence, Payabvash, Seyedmehdi, Mongan, John T, Cha, Soonmee, Aboian, Mariam S
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Brain cancer
Brain research
Cancer
Classification
Datasets
Deep learning
Diagnosis
Diagnostic imaging
Feature selection
Glioma
Gliomas
Health aspects
Learning algorithms
Machine learning
Medical prognosis
Neural networks
Oncology
Predictions
Review
Support vector machines
Technology application
Tumors
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Summary: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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14112623