Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features
BMC Cardiovascular Disorders, ISSN: 1471-2261, Vol: 21, Issue: 1, Page: 416
2021
- 6Citations
- 29Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations6
- Citation Indexes6
- Captures29
- Readers29
- 29
Article Description
Background: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiveness of CCTA for myocardial ischaemia patients was explored. Methods: This was a retrospective analysis of the CCTA images of 155 patients with clinically diagnosed coronary heart disease from September 2019 to January 2020, 79 of whom were considered positive (myocardial ischaemia) and 76 negative (normal myocardial blood supply) according to their clinical diagnoses. By using the deep learning model-based CQK software, the myocardium was automatically segmented from the CCTA images and used to extract texture features. All patients were randomly divided into a training cohort and a test cohort at a 7:3 ratio. The Spearman correlation and least absolute shrinkage and selection operator (LASSO) method were used for feature selection. Based on the selected features of the training cohort, a multivariable logistic regression model was established. Finally, the test cohort was used to verify the regression model. Results: A total of 387 features were extracted from the CCTA images of the 155 coronary heart disease patients. After performing dimensionality reduction with the Spearman correlation and LASSO, three texture features were selected. The accuracy, area under the curve, specificity, sensitivity, positive predictive value and negative predictive value of the constructed multivariable logistic regression model with the test cohort were 0.783, 0.875, 0.733, 0.875, 0.650 and 0.769, respectively. Conclusion: CCTA imaging texture features of the myocardium have potential as biomarkers for diagnosing myocardial ischaemia.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85113944183&origin=inward; http://dx.doi.org/10.1186/s12872-021-02206-z; http://www.ncbi.nlm.nih.gov/pubmed/34465308; https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-021-02206-z; https://dx.doi.org/10.1186/s12872-021-02206-z
Springer Science and Business Media LLC
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