Evidence map›Paper›PMID 41442001›Full record

ArticleEuropean radiology2026

Integrated assessment of total airway count and pneumonia volume on chest computed tomography as a prognostic biomarker for coronavirus disease.

Kensuke Nakagawara, Naoya Tanabe, Shotaro Chubachi, Tomoki Maetani, Yusuke Shiraishi, Takanori Asakura, Ho Namkoong, Hiromu Tanaka, Takashi Shimada, Shuhei Azekawa and 23 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

33 authors.

Kensuke Nakagawara *Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Naoya Tanabe *Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Shotaro ChubachiDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan. bachibachi472000@keio.jp.ORCID http://orcid.org/0000-0002-5046-3762
Tomoki MaetaniDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yusuke ShiraishiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takanori AsakuraDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Ho NamkoongDepartment of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan.
Hiromu TanakaDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Takashi ShimadaDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Shuhei AzekawaDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Shiro OtakeDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Takahiro FukushimaDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Mayuko WataseDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Hideki TeraiDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Mamoru SasakiInternal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan.
Soichiro UedaInternal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan.
Yukari KatoDepartment of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.
Norihiro HaradaDepartment of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.
Shoji SuzukiDepartment of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan.
Shuichi YoshidaDepartment of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan.
Hiroki TatenoDepartment of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan.
Yoshitake YamadaDepartment of Radiology, Keio University School of Medicine, Tokyo, Japan.
Masahiro JinzakiDepartment of Radiology, Keio University School of Medicine, Tokyo, Japan.
Toyohiro HiraiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yukinori OkadaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
Ryuji KoikeHealth Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan.
Makoto IshiiDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Akinori KimuraInstitute of Research, Tokyo Medical and Dental University, Tokyo, Japan.
Seiya ImotoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Satoru MiyanoM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan.
Seishi OgawaDepartment of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan.
Takanori KanaiDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Koichi FukunagaDivision of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.

Funding

Core Research for Evolutional Science and Technology JPMJCR20H2Ministry of Health, Labour and Welfare 20CA2054Precursory Research for Embryonic Science and Technology JPMJPR21R7the Japan Agency for Medical Research and Development JP20fk0108415the Japan Agency for Medical Research and Development JP20fk0108452the Japan Agency for Medical Research and Development JP20nk0101612the Japan Agency for Medical Research and Development JP21fk0108431the Japan Agency for Medical Research and Development JP21fk0108510the Japan Agency for Medical Research and Development JP21fk0108553the Japan Agency for Medical Research and Development JP21fk0108563the Japan Agency for Medical Research and Development JP21fk0108573the Japan Agency for Medical Research and Development JP21jk0210034the Japan Agency for Medical Research and Development JP21km0405211the Japan Agency for Medical Research and Development JP21km0405217the Japan Agency for Medical Research and Development JP21wm0325031the Japan Agency for Medical Research and Development JP22fk0108513the Japan Agency for Medical Research and Development JP22fk0108537the Japan Agency for Medical Research and Development JP22wm0325031the Japan Agency for Medical Research and Development JP23tm0524008
6 · The paper itself

Abstract

objectivesThe clinical relevance of computed tomography (CT)-based airway tree structure is unclear. Herein, we used artificial intelligence to segment the airway tree and pneumonia regions, measuring total airway count (TAC) and pneumonia volume to examine whether their combination is more closely associated with clinical outcomes in patients with coronavirus disease (COVID-19) than pneumonia volume alone. MATERIALS AND

methodsWe examined clinical data and chest CT from 781 hospitalized COVID-19 patients in a multicenter retrospective cohort in Japan, focusing on the percentage of critical outcomes (high-flow oxygen, invasive mechanical ventilation, or death). Additionally, 197 patients were followed up for 3 months to monitor TAC and pneumonia volume.

resultsCritical outcomes were observed in 63 (8.8%) patients, with higher TAC in those patients. Patients were divided into four groups based on cutoff values of 17.6% for pneumonia volume percent and 255 for TAC: Group A (low TAC, low pneumonia volume), Group B (high TAC, low pneumonia volume), Group C (low TAC, high pneumonia volume), and Group D (high TAC, high pneumonia volume). Group D had the worst outcomes, highest levels of inflammation, fibrosis markers, and complications, as well as a significantly higher risk of critical outcomes after adjusting for age, body mass index, sex, total lung volume and comorbidities. In the 3-month longitudinal analysis, pneumonia volume, but not TAC, improved in critical cases.

conclusionsThe integrated assessment of TAC and pneumonia volume effectively predicted critical outcomes in COVID-19 patients and may be useful for various respiratory diseases, including infectious or interstitial pneumonia. KEY POINTS: Question Total airway counts (TAC) on computed tomography (CT) scan is associated with respiratory disease progression, but clinical relevance of CT-based airway tree structure is unclear. Findings The integrated assessment of TAC and pneumonia volume effectively predicted critical outcomes in COVID-19 patients. Clinical relevance This metric can potentially be applied to various respiratory diseases, including infectious or interstitial pneumonia.

Indexed as

COVID-19LungTomography, X-Ray ComputedAdultAgedArtificial IntelligenceFemaleHumansJapanMaleMiddle AgedPrognosisRetrospective StudiesSARS-CoV-2Artificial intelligence (AI)-based analysisCOVID-19CTSARS-CoV-2 infectionTotal airway count

Identifiers

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.