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Published on: August 28, 2018
Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram
You-Chang Yang1, Yang Dou2, Zhi-Wei Wang2
1Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong, China.
Insights
Predicting myocardial ischemia in coronary heart disease (CHD) patients is improved by combining coronary computed tomography angiography (CCTA) radiomic features with clinical data. This combined approach shows significant clinical value for accurate diagnosis and patient management.
Area of Science:
- Cardiology
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Coronary heart disease (CHD) poses a significant health burden.
- Accurate prediction of myocardial ischemia is crucial for effective patient management.
- Coronary computed tomography angiography (CCTA) offers detailed anatomical information.
Purpose of the Study:
- To develop and validate a predictive model for myocardial ischemia in CHD patients.
- To assess the utility of combining CCTA-derived radiomic features with clinical factors.
- To enhance diagnostic accuracy beyond traditional methods.
Main Methods:
- Retrospective analysis of 110 CHD patients' CCTA and clinical data.
- Segmentation of left ventricular myocardium and extraction of radiomic features using specialized software.
- Development of independent radiomic and combined prediction models.
- Validation using training, internal, and external datasets with ROC curve and decision curve analysis.
Main Results:
- The combined model, incorporating clinical factors and radiomic features, demonstrated strong predictive performance.
- Area under the ROC curve (AUC) for the combined model reached 0.873 (training), 0.810 (internal validation), and 0.800 (external validation).
- Calibration curves confirmed good agreement between predicted and observed probabilities of myocardial ischemia.
Conclusions:
- Radiomic features from CCTA, when combined with clinical factors, provide significant clinical value for predicting myocardial ischemia in CHD patients.
- The developed combined model offers a promising tool for improving diagnostic accuracy and guiding treatment decisions.
- This approach highlights the potential of radiomics in non-invasive cardiovascular disease assessment.
Objective:
The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors.
Methods:
The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (n = 58) and normal myocardial blood supply (n = 52) groups. All patients were further categorized into training (n = 64) and internal validation (n = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis.
Results:
The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1-1, and the clinical value of nomogram was higher than that of clinical model.
Conclusion:
The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.
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