Author:
Bahar Ryan C.,Merkaj Sara,Cassinelli Petersen Gabriel I.,Tillmanns Niklas,Subramanian Harry,Brim Waverly Rose,Zeevi Tal,Staib Lawrence,Kazarian Eve,Lin MingDe,Bousabarah Khaled,Huttner Anita J.,Pala Andrej,Payabvash Seyedmehdi,Ivanidze Jana,Cui Jin,Malhotra Ajay,Aboian Mariam S.
Abstract
ObjectivesTo systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.MethodsThis 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.ResultsThe 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%).ConclusionsThe 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 RegistrationPROSPERO, identifier CRD42020209938.
Funder
Deutscher Akademischer Austauschdienst
National Institute of Diabetes and Digestive and Kidney Diseases
American Society of Neuroradiology
National Center for Advancing Translational Sciences
National Institutes of Health
Doris Duke Charitable Foundation
Nvidia
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