Evidence map›Paper›PMID 41378477›Full record

ArticleJournal of the American Heart Association2025

Chest Computed Tomography-Based Radiomics for the Diagnosis and Prognosis of Pulmonary Hypertension.

Binqian Ruan, Mengqi Chen, Qi Zhuang, Lihua Guan, Weiping Xie, Lan Wang, Qinhua Zhao, Shiyong Yu, An Wang, Jian Wang and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

12 authors.

Binqian RuanDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.ORCID 0000-0001-5340-6483
Mengqi ChenDepartment of Cardiology, Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou Guangdong China.ORCID 0000-0002-9178-5741
Qi ZhuangDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.ORCID 0000-0002-7165-2458
Lihua GuanDepartment of Cardiology, Zhongshan Hospital Fudan University, Shanghai Institute of Cardiovascular Disease Shanghai China.
Weiping XieDepartment of Respiratory and Critical Care Medicine The First Affiliated Hospital of Nanjing Medical University Nanjing Jiangsu China.
Lan WangDepartment of Cardio-Pulmonary Circulation, Shanghai Pulmonary Hospital Tongji University Shanghai China.ORCID 0000-0002-1990-3635
Qinhua ZhaoDepartment of Cardio-Pulmonary Circulation, Shanghai Pulmonary Hospital Tongji University Shanghai China.ORCID 0000-0003-4309-8880
Shiyong YuDepartment of Cardiology, the Second Affiliated Hospital Third Military Medical University (Army Medical University) Chongqing China.ORCID 0009-0001-5729-6063
An WangDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.ORCID 0000-0003-2442-445X
Jian WangDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.ORCID 0009-0003-6627-6202
Menghui YangDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.
Jieyan ShenDepartment of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.ORCID 0009-0005-8472-8107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImaging technique has emerged as an innovative tool for diagnosing and monitoring patients with pulmonary hypertension (PH). Current studies in radiomics primarily focus on hemodynamics and cardiac function, whereas the assessment of pulmonary vessels is often neglected. This study aims to investigate the diagnostic and prognostic value of computed tomography pulmonary vascular radiomics in PH.

methodsThis multicenter study enrolled 193 patients with PH and 193 controls (102 symptomatic non-PH cases and 91 healthy volunteers) for diagnostic analysis, with external validation in 38 patients with PH and 38 controls. For prognostic analysis, 166 patients with PH were prospectively followed (median follow-up: 16 months; 96 clinical deterioration events), with external validation in 32 patients with PH (median follow-up: 7 months; 11 events). Pulmonary vascular radiomics features extracted from chest computed tomography were used to develop predictive models for PH diagnosis and prognosis.

resultsThe diagnostic model integrating 7 radiomic and 3 clinical features achieved an area under the curve of 0.984 (95% CI, 0.959-0.995) in the derivation cohort and 0.980 (0.901-1.000) in external validation. For exploratory pulmonary arterial hypertension subtyping, the model incorporating 8 radiomic and 2 clinical features yielded an area under the curve of 0.898 (0.825-0.972) internally and 0.877 (0.352-1.000) externally. The prognostic radiomic-clinical model outperformed the European Society of Cardiology 4-strata risk assessment, with a 2-year area under the curve of 0.866 (0.8-0.942) versus 0.709 (0.648-0.789).

conclusionsThe radiomics-based models have strong diagnostic and prognostic capabilities for PH and can also successfully differentiate pulmonary arterial hypertension. This suggests the potential of radiomics to discern PH with different clinical risks, which may facilitate personalized drug therapy.

Indexed as

Hypertension, PulmonaryPulmonary ArteryTomography, X-Ray ComputedAdultAgedCase-Control StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisProspective StudiesRadiomicsReproducibility of Resultscomputed tomographydiagnosismachine learningprognosispulmonary hypertensionradiomics

Identifiers

PMID41378477
PMCPMC12826903

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Registered trials

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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 OpenQuestion graph.