Harmonization strategies for multicenter radiomics investigations. (2nd December 2020)

• Record Type: Journal Article
• Authors: Da-Ano, R; Visvikis, D; Hatt, M
• Is Part Of: Physics in medicine & biology. Volume 65:Number 24(2020:Dec.)
• Publication Date: 2020-12-02
• Subjects: radiomics -- batch effect removal -- deep learning -- data integration; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aba798
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called 'batch-effects' in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.