Intensity Harmonization Techniques Influence Radiomics Features and Radiomics-based Predictions in Sarcoma Patients
CROMBÉ, Amandine
Institut Bergonié [Bordeaux]
Modélisation Mathématique pour l'Oncologie [MONC]
Université de Bordeaux [UB]
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Institut Bergonié [Bordeaux]
Modélisation Mathématique pour l'Oncologie [MONC]
Université de Bordeaux [UB]
CROMBÉ, Amandine
Institut Bergonié [Bordeaux]
Modélisation Mathématique pour l'Oncologie [MONC]
Université de Bordeaux [UB]
Institut Bergonié [Bordeaux]
Modélisation Mathématique pour l'Oncologie [MONC]
Université de Bordeaux [UB]
SAUT, Olivier
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Modélisation Mathématique pour l'Oncologie [MONC]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Modélisation Mathématique pour l'Oncologie [MONC]
Language
en
Article de revue
This item was published in
Scientific Reports. 2020-12, vol. 10, n° 1
Nature Publishing Group
English Abstract
Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of ...Read more >
Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype with machine-learning to improve predictive models, such as metastasticrelapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogramRead less <
Origin
Hal imported