CT-based phenotyping of COVID-19: cluster analysis of pulmonary and extrapulmonary imaging markers from a multicentre retrospective cohort study

Coronavirus disease 2019 (COVID-19) displays a highly variable clinical course despite advancements in vaccination and antiviral therapies. Chest computed tomography (CT) has become a valuable tool for diagnosing and predicting COVID-19 severity; however, limited studies have explored integrating pulmonary and extrapulmonary CT markers to identify distinct clinical phenotypes. In this study, we aimed to evaluate the utility of cluster analysis, using quantitative pulmonary and extrapulmonary CT indicators, to classify patients with COVID-19 into distinct phenotypic clusters and assess their clinical relevance in predicting disease severity and outcomes. The primary outcome was the rate of critical outcomes (requiring high-flow oxygen therapy or invasive ventilator support or death). In this multicentre, retrospective cohort study, we analysed 1,034 patients with COVID-19 from four hospitals in Japan. Hierarchical cluster analysis was performed using age, sex, and seven imaging markers: pneumonia volume, muscle area, muscle density, subcutaneous and visceral fat indices, bone density, and coronary artery calcification score. Clinical characteristics, laboratory findings, and outcomes were compared across the identified clusters. Four distinct clusters were identified. Cluster 1 consisted of younger individuals with minimal pneumonia and favourable extrapulmonary organ markers, exhibiting the best clinical outcomes. Cluster 2 included younger patients with high pneumonia volume and visceral fat accumulation, exhibiting poor respiratory outcomes. Cluster 3 comprised older individuals with mild fat accumulation and low bone density, with intermediate severity. Cluster 4 presented the highest pneumonia volume, extensive visceral fat, and coronary artery calcification, resulting in the worst overall prognosis, including the highest mortality and in-hospital complications. Clustering based on pulmonary and extrapulmonary CT indicators enabled the precise classification of patients with COVID-19 into clinically significant subgroups with distinct outcomes. This study highlights the importance of integrating multiple imaging markers for disease phenotyping and prognosis.

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Journal
Scientific Reports
Published
2025-11-26
DOI
https://doi.org/10.1038/s41598-025-26187-7
Primary Topic
COVID-19 Clinical Research Studies
Type
article
Field-Weighted Citation Impact
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article

CT-based phenotyping of COVID-19: cluster analysis of pulmonary and extrapulmonary imaging markers from a multicentre retrospective cohort study

Seishi Ogawa, Shuichi Yoshida, Masahiro Kanai, Akinori Kimura et al.
Scientific Reports
COVID-19 Clinical Research Studies
article

CT-based phenotyping of COVID-19: cluster analysis of pulmonary and extrapulmonary imaging markers from a multicentre retrospective cohort study

Seishi Ogawa, Shuichi Yoshida, Masahiro Kanai, Akinori Kimura, Ryuji Koike, Shuhei Azekawa, Yukinori Okada, Soichiro Ueda, Mayuko Watase, Toyohiro Hirai, Naoya Tanabe, Hideki Terai, Takanori Asakura, Norihiro Harada, Tomoki Maetani, Ho Namkoong, Yusuke Shiraishi, Hiromu Tanaka, Kensuke Nakagawara, Yoshitake Yamada, Masahiro Jinzaki, Takahiro Fukushima, Shotaro Chubachi, Hiroki Tateno, Satoru Miyano, Seiya Imoto, Takashi Shimada, Makoto Ishii, Shoji Suzuki, Mamoru Sasaki, Yukari Kato, Shiro Otake, Koichi Fukunaga
article en

Abstract

Coronavirus disease 2019 (COVID-19) displays a highly variable clinical course despite advancements in vaccination and antiviral therapies. Chest computed tomography (CT) has become a valuable tool for diagnosing and predicting COVID-19 severity; however, limited studies have explored integrating pulmonary and extrapulmonary CT markers to identify distinct clinical phenotypes. In this study, we aimed to evaluate the utility of cluster analysis, using quantitative pulmonary and extrapulmonary CT indicators, to classify patients with COVID-19 into distinct phenotypic clusters and assess their clinical relevance in predicting disease severity and outcomes. The primary outcome was the rate of critical outcomes (requiring high-flow oxygen therapy or invasive ventilator support or death). In this multicentre, retrospective cohort study, we analysed 1,034 patients with COVID-19 from four hospitals in Japan. Hierarchical cluster analysis was performed using age, sex, and seven imaging markers: pneumonia volume, muscle area, muscle density, subcutaneous and visceral fat indices, bone density, and coronary artery calcification score. Clinical characteristics, laboratory findings, and outcomes were compared across the identified clusters. Four distinct clusters were identified. Cluster 1 consisted of younger individuals with minimal pneumonia and favourable extrapulmonary organ markers, exhibiting the best clinical outcomes. Cluster 2 included younger patients with high pneumonia volume and visceral fat accumulation, exhibiting poor respiratory outcomes. Cluster 3 comprised older individuals with mild fat accumulation and low bone density, with intermediate severity. Cluster 4 presented the highest pneumonia volume, extensive visceral fat, and coronary artery calcification, resulting in the worst overall prognosis, including the highest mortality and in-hospital complications. Clustering based on pulmonary and extrapulmonary CT indicators enabled the precise classification of patients with COVID-19 into clinically significant subgroups with distinct outcomes. This study highlights the importance of integrating multiple imaging markers for disease phenotyping and prognosis.

Scientific ReportsVol. 15(1)
Tokyo University of Science (JP), Keio University (JP), Kyoto University (JP), Juntendo University (JP), Keio University Hospital (JP), University of Tokyo Hospital (JP), Saitama Municipal Hospital (JP), RIKEN Center for Integrative Medical Sciences (JP), Kitasato Institute Hospital (JP), Institute of Science Tokyo (JP), Nagoya University (JP), Kitasato University (JP), The University of Tokyo (JP), Saitama Medical University (JP), Kindai University (JP), The University of Osaka (JP)
Japan Agency for Medical Research and Development, Keio University, Ministry of Health, Labour and Welfare, Japan Science and Technology Agency, Core Research for Evolutional Science and Technology, Precursory Research for Embryonic Science and Technology
Openalex Percentile: Top 57%
COVID-19 Clinical Research Studies
DOIOpen Access PDFOpenAlex
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