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High-dimensional detection of imaging response to treatment in multiple sclerosis
Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in co...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556513/ https://www.ncbi.nlm.nih.gov/pubmed/31304395 http://dx.doi.org/10.1038/s41746-019-0127-8 |
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author | Kanber, Baris Nachev, Parashkev Barkhof, Frederik Calvi, Alberto Cardoso, Jorge Cortese, Rosa Prados, Ferran Sudre, Carole H. Tur, Carmen Ourselin, Sebastien Ciccarelli, Olga |
author_facet | Kanber, Baris Nachev, Parashkev Barkhof, Frederik Calvi, Alberto Cardoso, Jorge Cortese, Rosa Prados, Ferran Sudre, Carole H. Tur, Carmen Ourselin, Sebastien Ciccarelli, Olga |
author_sort | Kanber, Baris |
collection | PubMed |
description | Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in common use. This hinders our ability to detect treatment effects, both in the management of individual patients and in interventional trials. Here we compared the ability of conventional models to detect an imaging response to treatment against high-dimensional models incorporating a wide multiplicity of imaging factors. We used fully-automated image analysis to extract 144 regional, longitudinal trajectories of pre- and post- treatment changes in brain volume and disconnection in a cohort of 124 natalizumab-treated patients. Low- and high-dimensional models of the relationship between treatment and the trajectories of change were built and evaluated with machine learning, quantifying performance with receiver operating characteristic curves. Simulations of randomised controlled trials enrolling varying numbers of patients were used to quantify the impact of dimensionality on statistical efficiency. Compared to existing methods, high-dimensional models were superior in treatment response detection (area under the receiver operating characteristic curve = 0.890 [95% CI = 0.885–0.895] vs. 0.686 [95% CI = 0.679–0.693], P < 0.01]) and in statistical efficiency (achieved statistical power = 0.806 [95% CI = 0.698–0.872] vs. 0.508 [95% CI = 0.403–0.593] with number of patients enrolled = 50, at α = 0.01). High-dimensional models based on routine, clinical imaging can substantially enhance the detection of the imaging response to treatment in multiple sclerosis, potentially enabling more accurate individual prediction and greater statistical efficiency of randomised controlled trials. |
format | Online Article Text |
id | pubmed-6556513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65565132019-07-12 High-dimensional detection of imaging response to treatment in multiple sclerosis Kanber, Baris Nachev, Parashkev Barkhof, Frederik Calvi, Alberto Cardoso, Jorge Cortese, Rosa Prados, Ferran Sudre, Carole H. Tur, Carmen Ourselin, Sebastien Ciccarelli, Olga NPJ Digit Med Article Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in common use. This hinders our ability to detect treatment effects, both in the management of individual patients and in interventional trials. Here we compared the ability of conventional models to detect an imaging response to treatment against high-dimensional models incorporating a wide multiplicity of imaging factors. We used fully-automated image analysis to extract 144 regional, longitudinal trajectories of pre- and post- treatment changes in brain volume and disconnection in a cohort of 124 natalizumab-treated patients. Low- and high-dimensional models of the relationship between treatment and the trajectories of change were built and evaluated with machine learning, quantifying performance with receiver operating characteristic curves. Simulations of randomised controlled trials enrolling varying numbers of patients were used to quantify the impact of dimensionality on statistical efficiency. Compared to existing methods, high-dimensional models were superior in treatment response detection (area under the receiver operating characteristic curve = 0.890 [95% CI = 0.885–0.895] vs. 0.686 [95% CI = 0.679–0.693], P < 0.01]) and in statistical efficiency (achieved statistical power = 0.806 [95% CI = 0.698–0.872] vs. 0.508 [95% CI = 0.403–0.593] with number of patients enrolled = 50, at α = 0.01). High-dimensional models based on routine, clinical imaging can substantially enhance the detection of the imaging response to treatment in multiple sclerosis, potentially enabling more accurate individual prediction and greater statistical efficiency of randomised controlled trials. Nature Publishing Group UK 2019-06-10 /pmc/articles/PMC6556513/ /pubmed/31304395 http://dx.doi.org/10.1038/s41746-019-0127-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kanber, Baris Nachev, Parashkev Barkhof, Frederik Calvi, Alberto Cardoso, Jorge Cortese, Rosa Prados, Ferran Sudre, Carole H. Tur, Carmen Ourselin, Sebastien Ciccarelli, Olga High-dimensional detection of imaging response to treatment in multiple sclerosis |
title | High-dimensional detection of imaging response to treatment in multiple sclerosis |
title_full | High-dimensional detection of imaging response to treatment in multiple sclerosis |
title_fullStr | High-dimensional detection of imaging response to treatment in multiple sclerosis |
title_full_unstemmed | High-dimensional detection of imaging response to treatment in multiple sclerosis |
title_short | High-dimensional detection of imaging response to treatment in multiple sclerosis |
title_sort | high-dimensional detection of imaging response to treatment in multiple sclerosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556513/ https://www.ncbi.nlm.nih.gov/pubmed/31304395 http://dx.doi.org/10.1038/s41746-019-0127-8 |
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