A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.

<h4>Purpose</h4>To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition...

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Autores principales:	Ronrick Da-Ano, François Lucia, Ingrid Masson, Ronan Abgral, Joanne Alfieri, Caroline Rousseau, Augustin Mervoyer, Caroline Reinhold, Olivier Pradier, Ulrike Schick, Dimitris Visvikis, Mathieu Hatt
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/715ee30ce0ed4be28a31be78d0d2b55f
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spelling	oai:doaj.org-article:715ee30ce0ed4be28a31be78d0d2b55f2021-12-02T20:09:48ZA transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.1932-620310.1371/journal.pone.0253653https://doaj.org/article/715ee30ce0ed4be28a31be78d0d2b55f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253653https://doaj.org/toc/1932-6203<h4>Purpose</h4>To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center.<h4>Material and methods</h4>The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines.<h4>Results</h4>The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available.<h4>Conclusion</h4>The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.Ronrick Da-AnoFrançois LuciaIngrid MassonRonan AbgralJoanne AlfieriCaroline RousseauAugustin MervoyerCaroline ReinholdOlivier PradierUlrike SchickDimitris VisvikisMathieu HattPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253653 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ronrick Da-Ano François Lucia Ingrid Masson Ronan Abgral Joanne Alfieri Caroline Rousseau Augustin Mervoyer Caroline Reinhold Olivier Pradier Ulrike Schick Dimitris Visvikis Mathieu Hatt A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
description	<h4>Purpose</h4>To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center.<h4>Material and methods</h4>The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines.<h4>Results</h4>The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available.<h4>Conclusion</h4>The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.
format	article
author	Ronrick Da-Ano François Lucia Ingrid Masson Ronan Abgral Joanne Alfieri Caroline Rousseau Augustin Mervoyer Caroline Reinhold Olivier Pradier Ulrike Schick Dimitris Visvikis Mathieu Hatt
author_facet	Ronrick Da-Ano François Lucia Ingrid Masson Ronan Abgral Joanne Alfieri Caroline Rousseau Augustin Mervoyer Caroline Reinhold Olivier Pradier Ulrike Schick Dimitris Visvikis Mathieu Hatt
author_sort	Ronrick Da-Ano
title	A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
title_short	A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
title_full	A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
title_fullStr	A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
title_full_unstemmed	A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
title_sort	transfer learning approach to facilitate combat-based harmonization of multicentre radiomic features in new datasets.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/715ee30ce0ed4be28a31be78d0d2b55f
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A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.

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