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Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect

PURPOSE: Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease...

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Autores principales: Ohno, Yoshiharu, Aoyagi, Kota, Arakita, Kazumasa, Doi, Yohei, Kondo, Masashi, Banno, Sumi, Kasahara, Kei, Ogawa, Taku, Kato, Hideaki, Hase, Ryota, Kashizaki, Fumihiro, Nishi, Koichi, Kamio, Tadashi, Mitamura, Keiko, Ikeda, Nobuhiro, Nakagawa, Atsushi, Fujisawa, Yasuko, Taniguchi, Akira, Ikeda, Hirotaka, Hattori, Hidekazu, Murayama, Kazuhiro, Toyama, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993669/
https://www.ncbi.nlm.nih.gov/pubmed/35396667
http://dx.doi.org/10.1007/s11604-022-01270-5
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author Ohno, Yoshiharu
Aoyagi, Kota
Arakita, Kazumasa
Doi, Yohei
Kondo, Masashi
Banno, Sumi
Kasahara, Kei
Ogawa, Taku
Kato, Hideaki
Hase, Ryota
Kashizaki, Fumihiro
Nishi, Koichi
Kamio, Tadashi
Mitamura, Keiko
Ikeda, Nobuhiro
Nakagawa, Atsushi
Fujisawa, Yasuko
Taniguchi, Akira
Ikeda, Hirotaka
Hattori, Hidekazu
Murayama, Kazuhiro
Toyama, Hiroshi
author_facet Ohno, Yoshiharu
Aoyagi, Kota
Arakita, Kazumasa
Doi, Yohei
Kondo, Masashi
Banno, Sumi
Kasahara, Kei
Ogawa, Taku
Kato, Hideaki
Hase, Ryota
Kashizaki, Fumihiro
Nishi, Koichi
Kamio, Tadashi
Mitamura, Keiko
Ikeda, Nobuhiro
Nakagawa, Atsushi
Fujisawa, Yasuko
Taniguchi, Akira
Ikeda, Hirotaka
Hattori, Hidekazu
Murayama, Kazuhiro
Toyama, Hiroshi
author_sort Ohno, Yoshiharu
collection PubMed
description PURPOSE: Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting. MATERIALS AND METHODS: From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan–Meier method followed by Wilcoxon’s signed-rank test. RESULTS: % GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008). CONCLUSION: ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.
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spelling pubmed-89936692022-04-11 Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect Ohno, Yoshiharu Aoyagi, Kota Arakita, Kazumasa Doi, Yohei Kondo, Masashi Banno, Sumi Kasahara, Kei Ogawa, Taku Kato, Hideaki Hase, Ryota Kashizaki, Fumihiro Nishi, Koichi Kamio, Tadashi Mitamura, Keiko Ikeda, Nobuhiro Nakagawa, Atsushi Fujisawa, Yasuko Taniguchi, Akira Ikeda, Hirotaka Hattori, Hidekazu Murayama, Kazuhiro Toyama, Hiroshi Jpn J Radiol Original Article PURPOSE: Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting. MATERIALS AND METHODS: From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan–Meier method followed by Wilcoxon’s signed-rank test. RESULTS: % GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008). CONCLUSION: ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score. Springer Nature Singapore 2022-04-09 2022 /pmc/articles/PMC8993669/ /pubmed/35396667 http://dx.doi.org/10.1007/s11604-022-01270-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Article
Ohno, Yoshiharu
Aoyagi, Kota
Arakita, Kazumasa
Doi, Yohei
Kondo, Masashi
Banno, Sumi
Kasahara, Kei
Ogawa, Taku
Kato, Hideaki
Hase, Ryota
Kashizaki, Fumihiro
Nishi, Koichi
Kamio, Tadashi
Mitamura, Keiko
Ikeda, Nobuhiro
Nakagawa, Atsushi
Fujisawa, Yasuko
Taniguchi, Akira
Ikeda, Hirotaka
Hattori, Hidekazu
Murayama, Kazuhiro
Toyama, Hiroshi
Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title_full Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title_fullStr Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title_full_unstemmed Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title_short Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect
title_sort newly developed artificial intelligence algorithm for covid-19 pneumonia: utility of quantitative ct texture analysis for prediction of favipiravir treatment effect
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993669/
https://www.ncbi.nlm.nih.gov/pubmed/35396667
http://dx.doi.org/10.1007/s11604-022-01270-5
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