Publication Type Article
Authors Bahar R, Merkaj S, Cassinelli Petersen G, Tillmanns N, Subramanian H, Brim W, Zeevi T, Staib L, Kazarian E, Lin M, Bousabarah K, Huttner A, Pala A, Payabvash S, Ivanidze J, Cui J, Malhotra A, Aboian M
Journal Front Oncol
Volume 12
Pagination 856231
Date Published 04/22/2022
ISSN 2234-943X
Abstract OBJECTIVES: To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9. RESULTS: The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%). CONCLUSIONS: The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, identifier CRD42020209938.
DOI 10.3389/fonc.2022.856231
PubMed ID 35530302
PubMed Central ID PMC9076130
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