Harmonization strategies for multicenter radiomics investigations

Area of Science:

• Medical Imaging Analysis
• Radiomics
• Data Science

Background:

• Large multicenter studies are essential for clinical translation of radiomics.
• Integrating radiomic data from different institutions is challenging due to variability in acquisition and processing.
• This variability, termed 'batch effects,' can impact feature consistency and study reproducibility.

Purpose of the Study:

• To review existing methods for integrating radiomic data from multicenter studies.
• To discuss strategies for mitigating batch effects in radiomic feature analysis.
• To explore the potential of deep learning in addressing challenges in multicenter radiomics.

Main Methods:

• Literature review of data integration techniques for multicenter studies.
• Analysis of methods for reducing batch effects in radiomic feature extraction.
• Discussion of deep learning approaches for harmonizing radiomic data.

Main Results:

• Existing data integration methods aim to reduce unwanted variation caused by batch effects.
• Variability in scanner models, protocols, and reconstruction settings are key sources of batch effects.
• Deep learning shows promise for future solutions in multicenter radiomic studies.

Conclusions:

• Effective data integration and batch effect reduction are critical for multicenter radiomics.
• Standardization and advanced computational methods are needed for reliable clinical application.
• Deep learning offers a promising avenue for overcoming current limitations in multicenter radiomic data analysis.