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Published on: December 19, 2013
Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy
Chunqing Zhou1, Yi Xiao2, Longxi Li3
1Department of Nuclear Medicine, The Third Xiangya Hospital of Central South University, No.138, Tongzipo Road, Changsha, Hunan Province, 410013, China.
Insights
A new radiomics nomogram using gated myocardial perfusion imaging (GMPI) effectively distinguishes between ischemic and non-ischemic heart failure origins. This tool improves diagnostic accuracy for personalized heart failure (HF) management.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Personalized management of heart failure (HF) is critical for improving patient outcomes.
- Distinguishing between ischemic and non-ischemic etiologies of HF is essential for targeted treatment strategies.
Purpose of the Study:
- To evaluate the effectiveness of a radiomics nomogram derived from gated myocardial perfusion imaging (GMPI) in differentiating ischemic from non-ischemic heart failure.
- To develop a predictive model that integrates radiomics features and clinical data for improved etiological diagnosis of heart failure with reduced ejection fraction (HFrEF).
Main Methods:
- Radiomics features were extracted from GMPI scans of 172 patients with HFrEF.
- Machine learning algorithms were employed to construct radiomics models and a radiomics nomogram incorporating clinical factors.
- Model performance was assessed using receiver operating characteristic curves, calibration curves, decision curve analysis, integrated discrimination improvement (IDI), and net reclassification index (NRI).
Main Results:
- The developed radiomics nomogram, integrating radiomics score, age, systolic blood pressure, and total perfusion deficit (TPD), significantly outperformed the conventional GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) in the validation set (AUC 0.853 vs. 0.707).
- The nomogram demonstrated a 28.3% improvement in diagnostic accuracy compared to the GMPI model, as indicated by IDI analysis.
- Three optimal radiomics features were identified for the radiomics model, and TPD was a key factor in the GMPI model.
Conclusions:
- A GMPI-based radiomics nomogram can accurately identify the ischemic etiology of HFrEF.
- Integrating radiomics signatures with clinical indicators offers a promising approach for enhancing the etiological diagnosis of heart failure.
- This tool supports personalized management strategies for patients with heart failure.
Abstract:
Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans. Radiomics features were extracted from the resting GMPI. Four machine learning algorithms were used to construct radiomics models, and the model with the best performances were selected to calculate the Radscore. A radiomics nomogram was constructed based on the Radscore and independent clinical factors. Finally, the model performance was validated using operating characteristic curves, calibration curve, decision curve analysis, integrated discrimination improvement values (IDI), and the net reclassification index (NRI). Three optimal radiomics features were used to build a radiomics model. Total perfusion deficit (TPD) was identified as the independent factors of conventional GMPI metrics for building the GMPI model. In the validation set, the radiomics nomogram integrating the Radscore, age, systolic blood pressure, and TPD significantly outperformed the GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) (AUC 0.853 vs. 0.707, p = 0.038). IDI analysis indicated that the nomogram improved diagnostic accuracy by 28.3% compared to the GMPI model in the validation set. By combining radiomics signatures with clinical indicators, we developed a GMPI-based radiomics nomogram that helps to identify the ischemic etiology of HFrEF.
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