Dual energy CT-based Radiomics for identification of myocardial focal scar and artificial beam-hardening.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, The Fifth Affiliated Hospital of Sun-Yat Sen University, Zhuhai, Guangdong 519000, China.
- Department of Cardiovascular Medicine, The Fifth Affiliated Hospital of Sun-Yat Sen University, Zhuhai, Guangdong 519000, China.
- Brain and Mind institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
- Department of CT&MRI, Friendship Hospital of Ili Kazak Autonomous Prefecture, Ili, Xinjiang, Urgur Autonomous Region 835000, China.
- Department of Radiology, The Fifth Affiliated Hospital of Sun-Yat Sen University, Zhuhai, Guangdong 519000, China. Electronic address: [email protected].
- Department of Cardiovascular Medicine, The Fifth Affiliated Hospital of Sun-Yat Sen University, Zhuhai, Guangdong 519000, China. Electronic address: [email protected].
- Department of Radiology, The Fifth Affiliated Hospital of Sun-Yat Sen University, Zhuhai, Guangdong 519000, China; Guangdong Provincial Engineering Research Center of Molecular Imaging, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong 519000, China. Electronic address: [email protected].
Abstract
Computed tomography is an inadequate method for detecting myocardial focal scar (MFS) due to its moderate density resolution, which is insufficient for distinguishing MFS from artificial beam-hardening (BH). Virtual monochromatic images (VMIs) of dual-energy coronary computed tomography angiography (DECCTA) provide a variety of diagnostic information with significant potential for detecting myocardial lesions. The aim of this study was to assess whether radiomics analysis in VMIs of DECCTA can help distinguish MFS from BH. A prospective cohort of patients who were suspected with an old myocardial infarction was assembled at two different centers between Janurary 2021 and June 2024. MFS and BH segmentation and radiomics feature extraction and selection were performed on VMIs images, and four machine learning classifiers were constructed using selected strongest features. Subsequently, an independent validation was conducted, and a subjective diagnosis of the validation set was provided by an radiologist. The AUC was used to assess the performance of the radiomics models. The training set included 57 patients from center 1 (mean age, 54 years +/- 9, 55 men), and the external validation set included 10 patients from center 2 (mean age, 59 years +/- 10, 9 men). The radiomics models exhibited the highest AUC value of 0.937 (expressed at 130 keV VMIs), while the radiologist demonstrated the highest AUC value of 0.734 (expressed at 40 keV VMIs). The integration of radiomic features derived from VMIs of DECCTA with machine learning algorithms has the potential to improve the efficiency of distinguishing MFS from BH.