What is the reason for and aim of the publication?
Cardiovascular imaging is used frequently in contemporary clinical trials, serving not only for participant selection but also for evaluating signals of efficacy, safety, and disease mechanisms.
The aim of this Scientific Statement is to provide a practical reference framework for implementing cardiovascular imaging in clinical trials. It outlines principles for selecting and validating imaging endpoints, standardizing imaging protocols, ensuring quality control, and integrating emerging technologies such as artificial intelligence, thereby supporting the generation of robust, reproducible, and clinically meaningful evidence.
What are the most important take-home messages?
- Imaging endpoints must be fit for purpose. Their selection should be driven by the scientific question and supported by evidence of clinical, analytical, and operational validity.
- Standardization is essential. Harmonized acquisition protocols, image analysis procedures, reporting standards, and quality assurance are critical to reducing variability, particularly in multicentre clinical trial settings.
- Core imaging laboratories play a central role. Rigorous training of study sites in imaging protocol and data acquisition, centralized image analysis, and continuous quality control improve consistency, reproducibility, and data integrity.
- Interpretability matters as much as statistical significance. Trial design should consider concepts such as minimal clinically important change in an imaging biomarker and minimal detectable change to ensure that observed imaging changes are clinically meaningful.
- Artificial intelligence offers major opportunities but requires careful validation. AI can improve efficiency and scalability of image analysis, but algorithms must be rigorously validated and integrated into well-governed clinical trial workflows before widespread implementation.
What are the challenges in practical implementation – and possible solutions?
One of the key challenges is the heterogeneity of imaging equipment, software platforms, acquisition protocols, and operator expertise across participating centres. This variability can introduce measurement bias and reduce reproducibility.
Potential solutions include:
- Developing standardized imaging protocols before trial initiation.
- Providing comprehensive training and certification for imaging personnel at participating sites.
- Using centralized core laboratories for image analysis whenever feasible.
- Implementing ongoing quality assurance, auditing, and feedback throughout the trial.
- Harmonizing imaging systems and analysis software across sites.
- Carefully validating AI-assisted workflows before deployment in pivotal studies.
Another challenge, when imaging endpoints are used in phase 2 clinical trials to derisk phase 3 clinical trials, is the link between short-term change in imaging biomarker(s) and long-term change in clinical outcomes. This should be addressed, whenever possible, by the clinical validation of imaging endpoints.
Which issues still need to be tackled that are not yet addressed by the paper?
Several important areas require further research and consensus:
- Stronger evidence linking imaging surrogate endpoints with hard clinical outcomes across diverse diseases.
- Better methods for translating imaging biomarkers from preclinical studies into clinical applications.
- Standardized regulatory pathways and qualification processes for novel imaging biomarkers.
- Broader external validation of AI algorithms across different vendors, institutions, and patient populations.
- Expansion of education and specialized training to support the growing complexity of multimodality imaging in clinical trials.
What further developments on the topic are emerging?
The field is rapidly evolving toward more quantitative, automated, and data-driven imaging. Emerging developments include:
- Increasing use of AI and machine learning for automated image acquisition, analysis, and quality control.
- Integration of multimodality imaging to provide complementary structural, functional, and molecular information in a non-invasive fashion.
- Development and validation of novel quantitative imaging biomarkers suitable for use as trial endpoints.
- Greater harmonization of imaging standards across international multicentre studies.
- Integration of imaging data with clinical, biomarker, genomic, and digital health data to enable more precise assessment of treatment effects and personalized medicine approaches.
Reference framework for implementation of cardiovascular imaging in clinical trials
Surkova E, Gimelli A, Giannopoulos AA, et al. Reference Framework for Implementation of Cardiovascular Imaging in Clinical Trials. A Scientific Statement of the European Association of Cardiovascular Imaging (EACVI) of the ESC. Eur Heart J Cardiovasc Imaging. Published online July 1, 2026. https://doi.org/10.1093/ehjci/jeag171
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