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Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study
OBJECTIVES: Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. METHODS: We retrospectively recruited 454 patients with...
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/PMC10067016/ https://www.ncbi.nlm.nih.gov/pubmed/37004571 http://dx.doi.org/10.1007/s00330-023-09589-x |
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author | Li, Ye Xu, Zexuan Lv, Xinna Li, Chenghai He, Wei Lv, Yan Hou, Dailun |
author_facet | Li, Ye Xu, Zexuan Lv, Xinna Li, Chenghai He, Wei Lv, Yan Hou, Dailun |
author_sort | Li, Ye |
collection | PubMed |
description | OBJECTIVES: Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. METHODS: We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves. RESULTS: Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05). CONCLUSIONS: The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB. CLINICAL RELEVANCE STATEMENT: Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients. KEY POINTS: • This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09589-x. |
format | Online Article Text |
id | pubmed-10067016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-100670162023-04-03 Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study Li, Ye Xu, Zexuan Lv, Xinna Li, Chenghai He, Wei Lv, Yan Hou, Dailun Eur Radiol Chest OBJECTIVES: Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. METHODS: We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves. RESULTS: Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05). CONCLUSIONS: The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB. CLINICAL RELEVANCE STATEMENT: Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients. KEY POINTS: • This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09589-x. Springer Berlin Heidelberg 2023-04-01 /pmc/articles/PMC10067016/ /pubmed/37004571 http://dx.doi.org/10.1007/s00330-023-09589-x Text en © The Author(s), under exclusive licence to European Society of Radiology 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Chest Li, Ye Xu, Zexuan Lv, Xinna Li, Chenghai He, Wei Lv, Yan Hou, Dailun Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title_full | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title_fullStr | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title_full_unstemmed | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title_short | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
title_sort | radiomics analysis of lung ct for multidrug resistance prediction in active tuberculosis: a multicentre study |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067016/ https://www.ncbi.nlm.nih.gov/pubmed/37004571 http://dx.doi.org/10.1007/s00330-023-09589-x |
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