Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prospective chest CT cohort with opportunistic multidimensional phenotype capture in routine care at a tertiary hospital in western China: Protocol.

BMJ open - 23 Sept 2026

Zhang Heng, Gong Caipan, Xiang Chunfeng, Wu Shucai, Liang Xifeng, Song Huazhang, Xiang Yanhan, Liang Shuang, Peng Xiaoling, Ren Xiaogang, Li Yang, Li Hongyan, Song Jing, Cheng Yuanhua, Xiang Yingchun, Wu Zhiwei, Zhao Chungang

Abstract excerpt

INTRODUCTION: Routine chest CT contains clinically relevant multidomain information beyond the immediate examination indication, but translation into routine care requires more than identifying individual biomarkers. This protocol describes a prospective observational cohort designed to evaluate whether technical assessability, standardised multidomain research phenotyping, routine radiology reporting and...

Read the complete abstract on PubMed

Topics

Humans
Tertiary Care Centers
Prospective Studies
China
Phenotype

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Tomography, X-Ray Computed

Research Design

Radiography, Thoracic

Source

Open in PubMed Open full text

Identifiers

PMID: 42778254
DOI: 10.1136/bmjopen-2026-121793
PMCID: PMC13629859

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 24 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prospective chest CT cohort with opportunistic multidimensional phenotype capture in routine care at a tertiary hospital in western China: Protocol.PMID 42778254Read ↗

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.Semantic proximity Read ↗

Chest radiographic phenotypes in post-tuberculosis lung disease: morphometric heterogeneity, functional outcomes, and implementation of scoring systems.Semantic proximity Read ↗

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.Semantic proximity Read ↗

Long-Term Pulmonary Consequences of Moderate and Severe COVID-19: A Functional Imaging Protocol for Clinical ScreeningSemantic proximity Read ↗

The clinical relevance of chest computed tomography imaging in pulmonary hypertension associated with lung diseases.Semantic proximity Read ↗

Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial.Semantic proximity Read ↗

Establishment of seven lung ultrasound phenotypes: a retrospective observational studySemantic proximity Read ↗

Establishment of seven lung ultrasound phenotypes: a retrospective observational study of an LUS registry.Semantic proximity Read ↗

Predictive validity of the simplified Radiographic Assessment of Lung Edema score for the mortality in critically ill COVID-19 patients with the B.1.617.2 (Delta) variant in Vietnam: a single-centre, cross-sectional studySemantic proximity Read ↗

Subtyping COPD by Using Visual and Quantitative CT Imaging Features.Semantic proximity Read ↗

Harmonization of Respiratory Data From 9 US Population-Based Cohorts: The NHLBI Pooled Cohorts Study.Semantic proximity Read ↗

Radiomics of the Airway (RadAr): Multi-Scale Airway Phenotyping for Disease Characterization on Routine CT ImagingSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.