CT and MRI radiomics in cardiovascular risk prediction: a systematic review and meta-analysis by the EuSoMII Radiomics Auditing Group

Question What is the current methodological quality and pooled diagnostic performance of cardiovascular radiomics for predicting clinical events, based on a meta-analysis? Findings The average METRICS quality score was 54.52%. A meta-analysis showed a pooled AUC of 0.81 for event prediction, but with high heterogeneity and publication bias. Clinical relevance Assessing radiomics research methodological quality is crucial to enhance reproducibility and clinical applicability of radiomics pipelines. The evaluation of cumulative evidence for cardiovascular events prediction may guide clinical translation and future study design.

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Publication Details

Journal
European Radiology
Published
2025-12-24
DOI
https://doi.org/10.1007/s00330-025-12236-2
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

CT and MRI radiomics in cardiovascular risk prediction: a systematic review and meta-analysis by the EuSoMII Radiomics Auditing Group

Mario Laudazi, Salvatore Claudio Fanni, Emmanouil Koltsakis, Renato Cuocolo et al.
1 citations
European Radiology
Radiomics and Machine Learning in Medical Imaging
1.36
article

CT and MRI radiomics in cardiovascular risk prediction: a systematic review and meta-analysis by the EuSoMII Radiomics Auditing Group

Mario Laudazi, Salvatore Claudio Fanni, Emmanouil Koltsakis, Renato Cuocolo, Carlo Di Donna, Armando Ugo Cavallo, Federica Vernuccio, Roberto Cannella, Bernardo Pereira, Tugba Akinci D’Antonoli, Andrea Ponsiglione
article en
1 citations

Abstract

Question What is the current methodological quality and pooled diagnostic performance of cardiovascular radiomics for predicting clinical events, based on a meta-analysis? Findings The average METRICS quality score was 54.52%. A meta-analysis showed a pooled AUC of 0.81 for event prediction, but with high heterogeneity and publication bias. Clinical relevance Assessing radiomics research methodological quality is crucial to enhance reproducibility and clinical applicability of radiomics pipelines. The evaluation of cumulative evidence for cardiovascular events prediction may guide clinical translation and future study design.

European Radiology
University of Pisa (IT), University of Salerno (IT), Karolinska University Hospital (SE), University Hospital of Basel (CH), Karolinska Institutet (SE), Istituto Dermopatico dell'Immacolata (IT), CTO Andrea Alesini (IT), Azienda Ospedaliera Sant'Andrea (IT), University Children’s Hospital Basel (CH), Center For Biomarker Research In Medicine (AT), Policlinico Tor Vergata (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), University of Naples Federico II (IT), University of Palermo (IT)
Università degli Studi di Palermo, Università degli Studi di Salerno, Università di Pisa
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
1.36
DOIOpen Access PDFOpenAlex
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