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Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting
OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. METHODS: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Ru...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973680/ https://www.ncbi.nlm.nih.gov/pubmed/35362751 http://dx.doi.org/10.1007/s00330-022-08730-6 |
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author | Nagaraj, Yeshaswini de Jonge, Gonda Andreychenko, Anna Presti, Gabriele Fink, Matthias A. Pavlov, Nikolay Quattrocchi, Carlo C. Morozov, Sergey Veldhuis, Raymond Oudkerk, Matthijs van Ooijen, Peter M. A. |
author_facet | Nagaraj, Yeshaswini de Jonge, Gonda Andreychenko, Anna Presti, Gabriele Fink, Matthias A. Pavlov, Nikolay Quattrocchi, Carlo C. Morozov, Sergey Veldhuis, Raymond Oudkerk, Matthijs van Ooijen, Peter M. A. |
author_sort | Nagaraj, Yeshaswini |
collection | PubMed |
description | OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. METHODS: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. RESULTS: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. CONCLUSION: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. KEYPOINTS: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08730-6. |
format | Online Article Text |
id | pubmed-8973680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-89736802022-04-01 Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting Nagaraj, Yeshaswini de Jonge, Gonda Andreychenko, Anna Presti, Gabriele Fink, Matthias A. Pavlov, Nikolay Quattrocchi, Carlo C. Morozov, Sergey Veldhuis, Raymond Oudkerk, Matthijs van Ooijen, Peter M. A. Eur Radiol Computed Tomography OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. METHODS: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. RESULTS: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. CONCLUSION: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. KEYPOINTS: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08730-6. Springer Berlin Heidelberg 2022-04-01 2022 /pmc/articles/PMC8973680/ /pubmed/35362751 http://dx.doi.org/10.1007/s00330-022-08730-6 Text en © The Author(s) 2022 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 | Computed Tomography Nagaraj, Yeshaswini de Jonge, Gonda Andreychenko, Anna Presti, Gabriele Fink, Matthias A. Pavlov, Nikolay Quattrocchi, Carlo C. Morozov, Sergey Veldhuis, Raymond Oudkerk, Matthijs van Ooijen, Peter M. A. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title | Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title_full | Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title_fullStr | Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title_full_unstemmed | Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title_short | Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
title_sort | facilitating standardized covid-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting |
topic | Computed Tomography |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973680/ https://www.ncbi.nlm.nih.gov/pubmed/35362751 http://dx.doi.org/10.1007/s00330-022-08730-6 |
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