tetano
Editor, Senior Moderator
Sci Rep
. 2025 Nov 26;15(1):42116.
doi: 10.1038/s41598-025-26187-7. CT-based phenotyping of COVID-19: cluster analysis of pulmonary and extrapulmonary imaging markers from a multicentre retrospective cohort study
Shiro Otake[SUP] #[/SUP][SUP] 1 2 [/SUP], Naoya Tanabe[SUP] #[/SUP][SUP] 3 [/SUP], Shotaro Chubachi[SUP] 4 [/SUP], Tomoki Maetani[SUP] 3 [/SUP], Yusuke Shiraishi[SUP] 3 [/SUP], Takanori Asakura[SUP] 1 5 6 [/SUP], Ho Namkoong[SUP] 7 [/SUP], Hiromu Tanaka[SUP] 1 [/SUP], Takashi Shimada[SUP] 1 [/SUP], Shuhei Azekawa[SUP] 1 [/SUP], Kensuke Nakagawara[SUP] 1 [/SUP], Takahiro Fukushima[SUP] 1 2 [/SUP], Mayuko Watase[SUP] 1 [/SUP], Hideki Terai[SUP] 1 [/SUP], Mamoru Sasaki[SUP] 8 [/SUP], Soichiro Ueda[SUP] 8 [/SUP], Yukari Kato[SUP] 9 [/SUP], Norihiro Harada[SUP] 9 [/SUP], Shoji Suzuki[SUP] 2 [/SUP], Shuichi Yoshida[SUP] 2 [/SUP], Hiroki Tateno[SUP] 2 [/SUP], Yoshitake Yamada[SUP] 10 [/SUP], Masahiro Jinzaki[SUP] 10 [/SUP], Toyohiro Hirai[SUP] 2 [/SUP], Yukinori Okada[SUP] 11 12 13 [/SUP], Ryuji Koike[SUP] 14 [/SUP], Makoto Ishii[SUP] 1 15 [/SUP], Akinori Kimura[SUP] 16 [/SUP], Seiya Imoto[SUP] 17 [/SUP], Satoru Miyano[SUP] 18 [/SUP], Seishi Ogawa[SUP] 19 20 21 [/SUP], Takanori Kanai[SUP] 22 [/SUP], Koichi Fukunaga[SUP] 1 [/SUP]
Affiliations
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.
Keywords: COVID-19; Cluster analysis; Computed tomography; Extrapulmonary markers.; Phenotyping; Pulmonary imaging.
. 2025 Nov 26;15(1):42116.
doi: 10.1038/s41598-025-26187-7. CT-based phenotyping of COVID-19: cluster analysis of pulmonary and extrapulmonary imaging markers from a multicentre retrospective cohort study
Shiro Otake[SUP] #[/SUP][SUP] 1 2 [/SUP], Naoya Tanabe[SUP] #[/SUP][SUP] 3 [/SUP], Shotaro Chubachi[SUP] 4 [/SUP], Tomoki Maetani[SUP] 3 [/SUP], Yusuke Shiraishi[SUP] 3 [/SUP], Takanori Asakura[SUP] 1 5 6 [/SUP], Ho Namkoong[SUP] 7 [/SUP], Hiromu Tanaka[SUP] 1 [/SUP], Takashi Shimada[SUP] 1 [/SUP], Shuhei Azekawa[SUP] 1 [/SUP], Kensuke Nakagawara[SUP] 1 [/SUP], Takahiro Fukushima[SUP] 1 2 [/SUP], Mayuko Watase[SUP] 1 [/SUP], Hideki Terai[SUP] 1 [/SUP], Mamoru Sasaki[SUP] 8 [/SUP], Soichiro Ueda[SUP] 8 [/SUP], Yukari Kato[SUP] 9 [/SUP], Norihiro Harada[SUP] 9 [/SUP], Shoji Suzuki[SUP] 2 [/SUP], Shuichi Yoshida[SUP] 2 [/SUP], Hiroki Tateno[SUP] 2 [/SUP], Yoshitake Yamada[SUP] 10 [/SUP], Masahiro Jinzaki[SUP] 10 [/SUP], Toyohiro Hirai[SUP] 2 [/SUP], Yukinori Okada[SUP] 11 12 13 [/SUP], Ryuji Koike[SUP] 14 [/SUP], Makoto Ishii[SUP] 1 15 [/SUP], Akinori Kimura[SUP] 16 [/SUP], Seiya Imoto[SUP] 17 [/SUP], Satoru Miyano[SUP] 18 [/SUP], Seishi Ogawa[SUP] 19 20 21 [/SUP], Takanori Kanai[SUP] 22 [/SUP], Koichi Fukunaga[SUP] 1 [/SUP]
Affiliations
- PMID: 41298698
- DOI: 10.1038/s41598-025-26187-7
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.
Keywords: COVID-19; Cluster analysis; Computed tomography; Extrapulmonary markers.; Phenotyping; Pulmonary imaging.