Objectives To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.Methods A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiotho-racic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication.Results Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase.Conclusion According to the results of this study, the majority of published radiomics models were not technically repro-ducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics.Clinical relevance statement To date, the reproducibility of radiomics research and open science practices within the radiom-ics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-shar ing practices will facilitate faster clinical translation.

Towards reproducible radiomics research: introduction of a database for radiomics studies

Cuocolo, Renato;
2023-01-01

Abstract

Objectives To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.Methods A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiotho-racic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication.Results Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase.Conclusion According to the results of this study, the majority of published radiomics models were not technically repro-ducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics.Clinical relevance statement To date, the reproducibility of radiomics research and open science practices within the radiom-ics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-shar ing practices will facilitate faster clinical translation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4840674
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