International journal of cardiology · 2025
Dual energy CT-based Radiomics for identification of myocardial focal scar and artificial beam-hardening.
СтатьяМногоцентровое исследованиеВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zeng Linqi, Hu Feng, Qin Peixin, Jia Taoyu, Lu Ling, Yang Zhengying, Zhou Xiaobing, Qiu Yuqing, Luo Liyun, Chen Bairong, Jin Lizi, Tang Wenyi, Wang Yanlin, Zhou Fang, Liu Tianmin, Wang Ani, Zhou Zhijuan, Guo Xiaosheng, Zheng Zhiwei, Fan Xiuwu, Xu Junwei, Xiao Li, Liu Qiue, Guan Weifeng, Chen Feifei, Wang Junyao, Li Shaolin, Chen Jian, Pan Cunxue
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULT: 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).
CONCLUSION: 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.
METHODS: 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.
RESULT: 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).
CONCLUSION: 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.