Evidence map›Paper›PMID 40036823›Full record

Trial reportEuropean heart journal. Cardiovascular Imaging2025

Development and validation of a radiomics model for detecting cardiac amyloidosis at coronary computed tomography angiography.

Qingchao Meng, Li Zhao, Xiaoxin Sun, Yang Wang, Lu Yu, U Joseph Schoepf, Akos Varga-Szemes, Dmitrij Kravchenko, Yining Wang, Hui Liu and 9 more

Abstract readRandomized Controlled TrialValidation Study
PubMed Publisher
In one paragraph

Trial report in European heart journal. Cardiovascular Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Qingchao MengDepartment of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
Li ZhaoDepartment of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
Xiaoxin SunNuclear Medicine, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
Yang WangMedical Research and Biometrics Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
Lu YuDepartment of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
U Joseph SchoepfDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Akos Varga-SzemesDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Dmitrij KravchenkoDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Yining WangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.ORCID 0000-0001-6441-2002
Hui LiuDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Yan ZhangDepartment of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Lei XuDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Xianbo YuCT Collaboration, Siemens Healthineers Ltd, Beijing, China.
Yubo GuoDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jiayu ChenDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Dong FengDepartment of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Kairui BoDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Yang GaoDepartment of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.
Bin LuDepartment of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing 100037, China.ORCID 0000-0002-1744-7584

Funding

National High Level Hospital Clinical Research 2023-GSP-GG-21
6 · The paper itself

Abstract

aimsTo investigate the diagnostic performance of computed tomography (CT)-based radiomics in detecting cardiac amyloidosis (CA) in patients with diffuse myocardial thickening. METHODS AND

resultsPatients with diffuse myocardial thickening who underwent coronary CT angiography were retrospectively enrolled from five hospitals. Patients from one hospital were randomly divided into training and internal test cohorts at a 7:3 ratio, and the other four hospitals constituted the external test cohort. The diagnosis of CA followed established guidelines. Regions of interest of myocardium were delineated to extract radiomics features to construct the radiomics model, and myocardial CT attenuation was measured. The diagnostic performance and clinical utility of the radiomics model and myocardial CT attenuation were compared with the area under the curve and decision curve analysis. The correlation between radiomics score and left ventricular function was analysed. A total of 378 patients (median age, 57 years; 257 men) were enrolled. Ten features were selected to construct the radiomics model. The areas under the curve of radiomics model were significantly higher than myocardial CT attenuation in the training (0.95 vs. 0.58, P < 0.001), internal test (0.95 vs. 0.59, P < 0.001), and external test cohorts (0.91 vs. 0.64, P < 0.001). Decision curve analysis indicated the radiomics model provided a greater net benefit than myocardial CT attenuation across cohorts. Radiomics scores were correlated with N-terminal proB-type natriuretic peptide and left ventricular diastolic diameter across cohorts (P < 0.05).

conclusionThe radiomics model exhibited good diagnostic performance for CA detection in patients with hypertrophic phenotypes, outperforming myocardial CT attenuation.

Indexed as

AmyloidosisCardiomyopathiesComputed Tomography AngiographyCoronary AngiographyAgedFemaleHumansMaleMiddle AgedRadiomicsRetrospective Studiescardiac amyloidosiscoronary CT angiographydiagnosisradiomics

Identifiers

PMID40036823

What OpenQuestion holds

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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.