Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease
Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion r...
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| Vydáno v: | Frontiers in neurology Ročník 14; s. 1105276 |
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Frontiers Media S.A
24.02.2023
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| ISSN: | 1664-2295, 1664-2295 |
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| Abstract | Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.
Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (
), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.
The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5
for FF and ± 1.8
for wT2.
This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence. |
|---|---|
| AbstractList | PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.MethodsTwenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.ResultsThe combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2.ConclusionThis pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence. Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.MethodsTwenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2.ResultsThe combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2.This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.ConclusionThis pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence. Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows ( ), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 for FF and ± 1.8 for wT2. This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence. |
| Author | Mira, Antonietta Paoletti, Matteo Barzaghi, Leonardo Figini, Silvia Tasca, Giorgio Monforte, Mauro Santini, Francesco Pichiecchio, Anna Deligianni, Xeni Colelli, Giulia Ricci, Enzo Manco, Giulia Bergsland, Niels |
| AuthorAffiliation | 4 UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS , Rome , Italy 2 Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation , Pavia , Italy 5 Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York , Buffalo, NY , United States 6 IRCCS, Fondazione Don Carlo Gnocchi ONLUS , Milan , Italy 12 Department of Political and Social Sciences, University of Pavia , Pavia , Italy 14 Department of Brain and Behavioural Sciences, University of Pavia , Pavia , Italy 8 Basel Muscle MRI, Department of Biomedical Engineering, University of Basel , Basel , Switzerland 9 John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts , Newcastle upon Tyne , United Kingdom 3 INFN, Group of Pavia , Pavia , Italy 11 Department of Science and High Technology, University of Insubria , Como , Italy 1 |
| AuthorAffiliation_xml | – name: 7 Department of Radiology, University Hospital Basel , Basel , Switzerland – name: 10 Data Science Lab, Università della Svizzera italiana , Lugano , Switzerland – name: 13 BioData Science Center, IRCCS Mondino Foundation , Pavia , Italy – name: 14 Department of Brain and Behavioural Sciences, University of Pavia , Pavia , Italy – name: 4 UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS , Rome , Italy – name: 12 Department of Political and Social Sciences, University of Pavia , Pavia , Italy – name: 2 Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation , Pavia , Italy – name: 8 Basel Muscle MRI, Department of Biomedical Engineering, University of Basel , Basel , Switzerland – name: 9 John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts , Newcastle upon Tyne , United Kingdom – name: 6 IRCCS, Fondazione Don Carlo Gnocchi ONLUS , Milan , Italy – name: 1 Department of Mathematics, University of Pavia , Pavia , Italy – name: 3 INFN, Group of Pavia , Pavia , Italy – name: 5 Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York , Buffalo, NY , United States – name: 11 Department of Science and High Technology, University of Insubria , Como , Italy |
| Author_xml | – sequence: 1 givenname: Giulia surname: Colelli fullname: Colelli, Giulia – sequence: 2 givenname: Leonardo surname: Barzaghi fullname: Barzaghi, Leonardo – sequence: 3 givenname: Matteo surname: Paoletti fullname: Paoletti, Matteo – sequence: 4 givenname: Mauro surname: Monforte fullname: Monforte, Mauro – sequence: 5 givenname: Niels surname: Bergsland fullname: Bergsland, Niels – sequence: 6 givenname: Giulia surname: Manco fullname: Manco, Giulia – sequence: 7 givenname: Xeni surname: Deligianni fullname: Deligianni, Xeni – sequence: 8 givenname: Francesco surname: Santini fullname: Santini, Francesco – sequence: 9 givenname: Enzo surname: Ricci fullname: Ricci, Enzo – sequence: 10 givenname: Giorgio surname: Tasca fullname: Tasca, Giorgio – sequence: 11 givenname: Antonietta surname: Mira fullname: Mira, Antonietta – sequence: 12 givenname: Silvia surname: Figini fullname: Figini, Silvia – sequence: 13 givenname: Anna surname: Pichiecchio fullname: Pichiecchio, Anna |
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| Cites_doi | 10.1186/s12891-020-03910-1 10.1109/TIT.1967.1053964 10.1111/j.2517-6161.1996.tb02080.x 10.1212/01.wnl.0000324927.28817.9b 10.1023/A:1010933404324 10.1002/ana.25804 10.1158/0008-5472.CAN-18-0125 10.1002/jmri.24619 10.1053/ejpn.2002.0617 10.1016/j.ejrad.2020.109460 10.1038/s41598-022-04817-8 10.1212/WNL.0000000000002640 10.1186/2044-5040-4-12 10.1007/s10334-021-00967-4 10.1002/wics.101 10.1002/jcsm.12473 10.1007/s00330-015-3890-1 10.2214/AJR.11.8233 10.1212/WNL.0000000000001783 10.1093/pnasnexus/pgac039 10.1002/jmri.20804 10.3389/fneur.2021.630387 10.1212/WNL.0000000000000828 10.3233/JND-160145 10.1016/j.neuroimage.2008.10.055 10.1016/j.neurol.2016.08.002 10.1212/WNL.0000000000000785 10.1186/s13244-020-00887-2 10.3389/fneur.2019.00078 10.1002/mus.23370 10.1016/j.mri.2012.05.001 10.1002/nbm.2851 10.1007/s00415-016-8361-3 10.1038/s41598-021-89311-3 10.1007/s00330-022-08862-9 10.1080/00401706.1970.10488634 10.1007/BF00868438 |
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| Copyright | Copyright © 2023 Colelli, Barzaghi, Paoletti, Monforte, Bergsland, Manco, Deligianni, Santini, Ricci, Tasca, Mira, Figini and Pichiecchio. Copyright © 2023 Colelli, Barzaghi, Paoletti, Monforte, Bergsland, Manco, Deligianni, Santini, Ricci, Tasca, Mira, Figini and Pichiecchio. 2023 Colelli, Barzaghi, Paoletti, Monforte, Bergsland, Manco, Deligianni, Santini, Ricci, Tasca, Mira, Figini and Pichiecchio |
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| Keywords | FSHD radiomics muscle MRI machine learning stir |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Jordi Diaz-Manera, University of Newcastle, United Kingdom These authors share last authorship Reviewed by: Teresa Gerhalter, University Hospital Erlangen, Germany; Jorge Alonso-Pérez, Hospital Santa Creu i Sant Pau, Spain; José Verdú-Díaz, Newcastle University, United Kingdom These authors share first authorship This article was submitted to Applied Neuroimaging, a section of the journal Frontiers in Neurology |
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| Title | Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease |
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