Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

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 tumors’ radiological phenotype with machine-learning to improve predict...

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Autores principales:	Amandine Crombé, Michèle Kind, David Fadli, François Le Loarer, Antoine Italiano, Xavier Buy, Olivier Saut
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/171eb74794744b88a09d9e582358e5b0
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spelling	oai:doaj.org-article:171eb74794744b88a09d9e582358e5b02021-12-02T18:48:01ZIntensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients10.1038/s41598-020-72535-02045-2322https://doaj.org/article/171eb74794744b88a09d9e582358e5b02020-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-72535-0https://doaj.org/toc/2045-2322Abstract 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 metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-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 histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: < 0.0001–0.02). Unsupervised clustering performed on each radiomics dataset showed that only clusters from the No-IHT, IHTstd, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.Amandine CrombéMichèle KindDavid FadliFrançois Le LoarerAntoine ItalianoXavier BuyOlivier SautNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amandine Crombé Michèle Kind David Fadli François Le Loarer Antoine Italiano Xavier Buy Olivier Saut Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
description	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 tumors’ radiological phenotype with machine-learning to improve predictive models, such as metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-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 histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: < 0.0001–0.02). Unsupervised clustering performed on each radiomics dataset showed that only clusters from the No-IHT, IHTstd, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.
format	article
author	Amandine Crombé Michèle Kind David Fadli François Le Loarer Antoine Italiano Xavier Buy Olivier Saut
author_facet	Amandine Crombé Michèle Kind David Fadli François Le Loarer Antoine Italiano Xavier Buy Olivier Saut
author_sort	Amandine Crombé
title	Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
title_short	Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
title_full	Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
title_fullStr	Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
title_full_unstemmed	Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
title_sort	intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/171eb74794744b88a09d9e582358e5b0
work_keys_str_mv	AT amandinecrombe intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT michelekind intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT davidfadli intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT francoisleloarer intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT antoineitaliano intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT xavierbuy intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients AT oliviersaut intensityharmonizationtechniquesinfluenceradiomicsfeaturesandradiomicsbasedpredictionsinsarcomapatients
_version_	1718377607284654080

Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

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