Minimising multi-centre radiomics variability through image normalisation: a pilot study
Open accessAbstract
Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
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Statistical features are affected by confounders such as inter-reader variability in segmentations and acquisition settings of scanners, limiting their applicability in diverse settings.
“Moreover, these statistical features are affected by several confounders, such as inter-reader variability in segmentations26 and acquisition settings of the scanners27, limiting their applicability in diverse settings.”
From Foundation Model for Cancer Imaging Biomarkers (2024) · cites this paper as [27]
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- Authors
- Campello, V. M., Martín-Isla, C., Izquierdo, C., Guala, A., Palomares, J. F. R., Viladés, D., Descalzo, M. L., Karakas, M., Çavuş, E., Raisi-Estabragh, Z., Petersen, S. E., Escalera, S., Seguí, S., & Lekadir, K.
- Affiliations
- Universitat de Barcelona (ES)
- Instituto de Salud Carlos III (ES)
- Universitat Autònoma de Barcelona (ES)
- University Medical Center Hamburg-Eppendorf (DE)
- Universität Hamburg (DE)
- St Bartholomew's Hospital (GB)
- Corresponding author
- Campello, Víctor M.
- ORCID
- Venue
- Scientific Reports
- Published
- 2022-07-22
- Volume
- 12
- Issue
- 1
- Pages
- 12532-12532
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9307565
- OpenAlex
- W4286487934
- Cited by
- 24
- References
- 25
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 4 · 2023: 10 · 2022: 1
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (publishedVersion, cc-by)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Funders
- Horizon 2020 (825903)
- Ministerio de Ciencia e Innovación (IJC2018-037349-I, RTI2018-099898-B-I00)
- British Heart Foundation (FS/17/81/33318)
- National Institute for Health Research
- Engineering and Physical Sciences Research Council (EP/P001009/1)
- European Regional Development Fund
- Ministerio de Economía y Competitividad (PID2019-105093GB-I00)
- Institució Catalana de Recerca i Estudis Avançats
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- radiomics, image normalization, combat, histogram matching, cardiovascular magnetic resonance, hypertrophic cardiomyopathy, texture features, left ventricular myocardium
- MeSH terms
- Cardiomyopathy, Hypertrophic, Humans, Magnetic Resonance Imaging, Pilot Projects
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