Self-reported checklists and quality scoring tools in radiomics: a meta-research

Objective To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. Methods Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori...

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Bibliographic Details
Published in:European radiology 2024-08, Vol.34 (8), p.5028-5040
Main Authors: Kocak, Burak, Akinci D’Antonoli, Tugba, Ates Kus, Ece, Keles, Ali, Kala, Ahmet, Kose, Fadime, Kadioglu, Mehmet, Solak, Sila, Sunman, Seyma, Temiz, Zisan Hayriye
Format: Article
Language:English
Subjects:
Annotations
Artificial intelligence
Catalysts
Check lists
Checklist
Diagnostic Imaging - methods
Diagnostic Imaging - standards
Diagnostic Radiology
Documentation
Documents
Humans
Imaging
Imaging Informatics and Artificial Intelligence
Internal Medicine
Interventional Radiology
Medical imaging
Medicine
Medicine & Public Health
Neuroradiology
Prediction models
Radiology
Radiology - methods
Radiology - standards
Radiomics
Self Report
Statistical analysis
Ultrasound
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Summary:Objective To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. Methods Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. Results The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test ( p  
ISSN:1432-1084
0938-7994
1432-1084
DOI:10.1007/s00330-023-10487-5