TY - JOUR
T1 - ESR Essentials
T2 - radiomics-practice recommendations by the European Society of Medical Imaging Informatics
AU - Santinha, Joao
AU - Pinto dos Santos, Daniel
AU - Laqua, Fabian
AU - Visser, Jacob J.
AU - Groot Lipman, Kevin B. W.
AU - Dietzel, Matthias
AU - Klontzas, Michail E.
AU - Cuocolo, Renato
AU - Gitto, Salvatore
AU - Akinci D'Antonoli, Tugba
N1 - This paper was endorsed by the Executive Council of the European Society of Radiology (ESR) and the European Society of Medical Imaging Informatics (EuSoMII) in September 2024.
PY - 2024/10/25
Y1 - 2024/10/25
N2 - Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics.
AB - Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics.
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=eur_pure&SrcAuth=WosAPI&KeyUT=WOS:001342172200001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1007/s00330-024-11093-9
DO - 10.1007/s00330-024-11093-9
M3 - Review article
C2 - 39453470
SN - 0938-7994
JO - European Radiology
JF - European Radiology
ER -