Evidence map›Paper›PMID 42825975›Full record

ArticleJournal of cardiovascular translational research2026

Deep Survival Modeling With Echocardiographic Myocardial Texture Radiomics for Prediction of Major Adverse Cardiovascular Events in Hypertrophic Cardiomyopathy.

Guizi Liang, Jiangyu Han, Xiaolan Huang, Xing Chen, Dongwei Xie, Limei Liang, Rentao Zhi, Shengjiang Chen, Bulin Zhang, Yan Deng

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 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
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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.

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Guizi Liang *Department of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Jiangyu Han *Department of Ultrasound Medical, Liuzhou People's Hospital Affiliated to Guangxi Medical University, 8 Wenchang Road, Liuzhou, Guangxi, 545006, China.
Xiaolan HuangDepartment of Ultrasound Medical, Liuzhou People's Hospital Affiliated to Guangxi Medical University, 8 Wenchang Road, Liuzhou, Guangxi, 545006, China.
Xing ChenDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Dongwei XieDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Limei LiangDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Rentao ZhiDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Shengjiang ChenDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
Bulin ZhangDepartment of Ultrasound Medical, Liuzhou People's Hospital Affiliated to Guangxi Medical University, 8 Wenchang Road, Liuzhou, Guangxi, 545006, China. 642157645@qq.com.
Yan DengDepartment of Ultrasound Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China. Dengyan@sr.gxmu.edu.cn.ORCID http://orcid.org/0009-0005-7260-1694

Funding

Guangxi medical "139" Program for Training High-level Backbone Talents G201903053Innovation Team of the First Affiliated Hospital of Guangxi Medical University YYZS2024003National Natural Science Foundation of China 82060051National Natural Science Foundation of China 82460082the Guangxi Natural Science Foundation 2023GXNSFAA026173the Precision Medicine Foundation of Guangxi Key Laboratory of Cardio-cerebrovascular Disease GXXNXG202301the project of Guangxi Health Commission of Self-funded Western Medicine Research Z-A20240482the projects of Guangxi Medical and Health Appropriate Technology Development and Promotion Application S2024018Young Leader Talent Training Program of Guangxi Medical University 202307
6 · The paper itself

Abstract

Hypertrophic cardiomyopathy (HCM) carries substantial risk of major adverse cardiovascular events (MACE), yet current tools do not fully exploit quantitative myocardial texture from routine echocardiography. We retrospectively studied 413 patients with HCM from two tertiary centers to develop and externally validate a deep survival model integrating transthoracic echocardiographic texture radiomics with clinical and conventional echocardiographic variables. Patients from one center (n = 308) were used for development and internal testing and those from the other (n = 105) for external validation. The best-performing model was selected by cross-validated Harrell C-index and interpreted using Shapley additive explanations. During median follow-up of 30.7 and 35.6 months, 83 patients (20.1%) experienced a first MACE. The Combined model showed the highest concordance and net clinical benefit, with age, RadScore, left atrial diameter, and maximal wall thickness as leading contributors. This model may provide an accessible approach to individualised MACE risk stratification in HCM.

Indexed as

Cardiomyopathy, HypertrophicDecision Support TechniquesDeep LearningEchocardiographyImage Interpretation, Computer-AssistedRadiomicsAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisReproducibility of ResultsRetrospective StudiesRisk AssessmentDeep survival modelEchocardiographic texture analysisEchocardiographyHypertrophic cardiomyopathyRadiomics

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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.