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Differentiating between common PSP phenotypes using structural MRI: a machine learning study
BACKGROUND: Differentiating Progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. METHODS: Sixty-two PSP-RS, 40 PSP-P patients and 33 control...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576703/ https://www.ncbi.nlm.nih.gov/pubmed/37507502 http://dx.doi.org/10.1007/s00415-023-11892-y |
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author | Quattrone, Andrea Sarica, Alessia Buonocore, Jolanda Morelli, Maurizio Bianco, Maria Giovanna Calomino, Camilla Aracri, Federica De Maria, Marida Vescio, Basilio Vaccaro, Maria Grazia Quattrone, Aldo |
author_facet | Quattrone, Andrea Sarica, Alessia Buonocore, Jolanda Morelli, Maurizio Bianco, Maria Giovanna Calomino, Camilla Aracri, Federica De Maria, Marida Vescio, Basilio Vaccaro, Maria Grazia Quattrone, Aldo |
author_sort | Quattrone, Andrea |
collection | PubMed |
description | BACKGROUND: Differentiating Progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. METHODS: Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. RESULTS: MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. CONCLUSION: The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-023-11892-y. |
format | Online Article Text |
id | pubmed-10576703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105767032023-10-16 Differentiating between common PSP phenotypes using structural MRI: a machine learning study Quattrone, Andrea Sarica, Alessia Buonocore, Jolanda Morelli, Maurizio Bianco, Maria Giovanna Calomino, Camilla Aracri, Federica De Maria, Marida Vescio, Basilio Vaccaro, Maria Grazia Quattrone, Aldo J Neurol Original Communication BACKGROUND: Differentiating Progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. METHODS: Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. RESULTS: MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. CONCLUSION: The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-023-11892-y. Springer Berlin Heidelberg 2023-07-29 2023 /pmc/articles/PMC10576703/ /pubmed/37507502 http://dx.doi.org/10.1007/s00415-023-11892-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Communication Quattrone, Andrea Sarica, Alessia Buonocore, Jolanda Morelli, Maurizio Bianco, Maria Giovanna Calomino, Camilla Aracri, Federica De Maria, Marida Vescio, Basilio Vaccaro, Maria Grazia Quattrone, Aldo Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title | Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title_full | Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title_fullStr | Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title_full_unstemmed | Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title_short | Differentiating between common PSP phenotypes using structural MRI: a machine learning study |
title_sort | differentiating between common psp phenotypes using structural mri: a machine learning study |
topic | Original Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576703/ https://www.ncbi.nlm.nih.gov/pubmed/37507502 http://dx.doi.org/10.1007/s00415-023-11892-y |
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