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Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia
BACKGROUND: Emerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at de...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289251/ https://www.ncbi.nlm.nih.gov/pubmed/34290581 http://dx.doi.org/10.3389/fnins.2021.682777 |
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author | Cui, Long-Biao Zhang, Ya-Juan Lu, Hong-Liang Liu, Lin Zhang, Hai-Jun Fu, Yu-Fei Wu, Xu-Sha Xu, Yong-Qiang Li, Xiao-Sa Qiao, Yu-Ting Qin, Wei Yin, Hong Cao, Feng |
author_facet | Cui, Long-Biao Zhang, Ya-Juan Lu, Hong-Liang Liu, Lin Zhang, Hai-Jun Fu, Yu-Fei Wu, Xu-Sha Xu, Yong-Qiang Li, Xiao-Sa Qiao, Yu-Ting Qin, Wei Yin, Hong Cao, Feng |
author_sort | Cui, Long-Biao |
collection | PubMed |
description | BACKGROUND: Emerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches. METHODS: A total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach. RESULTS: Using radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%. CONCLUSION: Our study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection. |
format | Online Article Text |
id | pubmed-8289251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82892512021-07-20 Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia Cui, Long-Biao Zhang, Ya-Juan Lu, Hong-Liang Liu, Lin Zhang, Hai-Jun Fu, Yu-Fei Wu, Xu-Sha Xu, Yong-Qiang Li, Xiao-Sa Qiao, Yu-Ting Qin, Wei Yin, Hong Cao, Feng Front Neurosci Neuroscience BACKGROUND: Emerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches. METHODS: A total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach. RESULTS: Using radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%. CONCLUSION: Our study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection. Frontiers Media S.A. 2021-07-05 /pmc/articles/PMC8289251/ /pubmed/34290581 http://dx.doi.org/10.3389/fnins.2021.682777 Text en Copyright © 2021 Cui, Zhang, Lu, Liu, Zhang, Fu, Wu, Xu, Li, Qiao, Qin, Yin and Cao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Cui, Long-Biao Zhang, Ya-Juan Lu, Hong-Liang Liu, Lin Zhang, Hai-Jun Fu, Yu-Fei Wu, Xu-Sha Xu, Yong-Qiang Li, Xiao-Sa Qiao, Yu-Ting Qin, Wei Yin, Hong Cao, Feng Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title | Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title_full | Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title_fullStr | Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title_full_unstemmed | Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title_short | Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia |
title_sort | thalamus radiomics-based disease identification and prediction of early treatment response for schizophrenia |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289251/ https://www.ncbi.nlm.nih.gov/pubmed/34290581 http://dx.doi.org/10.3389/fnins.2021.682777 |
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