Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study
ObjectiveTo develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).MethodsA total of 226 patients who received coronary computer tomogra...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1282768/full |
| _version_ | 1797386271245467648 |
|---|---|
| author | Jie Hou Jie Hou Hui Jin Hui Jin Yongsheng Zhang Yuyun Xu Feng Cui Xue Qin Lu Han Zhongyu Yuan Guangying Zheng Jiaxuan Peng Zhenyu Shu Xiangyang Gong |
| author_facet | Jie Hou Jie Hou Hui Jin Hui Jin Yongsheng Zhang Yuyun Xu Feng Cui Xue Qin Lu Han Zhongyu Yuan Guangying Zheng Jiaxuan Peng Zhenyu Shu Xiangyang Gong |
| author_sort | Jie Hou |
| collection | DOAJ |
| description | ObjectiveTo develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).MethodsA total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.ResultsCT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603–0.838] vs.0.711 [95% CI: 0.584–0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651–0.863] vs. 0.682 [95% CI: 0.547–0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815–0.956)].ConclusionpFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression. |
| first_indexed | 2024-03-08T22:06:51Z |
| format | Article |
| id | doaj.art-da040ee4994243ef84dfe7f40d2679e0 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-03-08T22:06:51Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-da040ee4994243ef84dfe7f40d2679e02023-12-19T08:30:52ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-12-011010.3389/fcvm.2023.12827681282768Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot studyJie Hou0Jie Hou1Hui Jin2Hui Jin3Yongsheng Zhang4Yuyun Xu5Feng Cui6Xue Qin7Lu Han8Zhongyu Yuan9Guangying Zheng10Jiaxuan Peng11Zhenyu Shu12Xiangyang Gong13Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaJinzhou Medical University, Jinzhou, Liaoning, ChinaRehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaBengbu Medical College, Bengbu, Anhui, ChinaThe Hangzhou TCM Hospital (Affiliated Zhejiang Chinese Medical University), Hangzhou, Zhejiang, ChinaRehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaThe Hangzhou TCM Hospital (Affiliated Zhejiang Chinese Medical University), Hangzhou, Zhejiang, ChinaBengbu Medical College, Bengbu, Anhui, ChinaJinzhou Medical University, Jinzhou, Liaoning, ChinaJinzhou Medical University, Jinzhou, Liaoning, ChinaJinzhou Medical University, Jinzhou, Liaoning, ChinaJinzhou Medical University, Jinzhou, Liaoning, ChinaRehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaRehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaObjectiveTo develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).MethodsA total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.ResultsCT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603–0.838] vs.0.711 [95% CI: 0.584–0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651–0.863] vs. 0.682 [95% CI: 0.547–0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815–0.956)].ConclusionpFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.https://www.frontiersin.org/articles/10.3389/fcvm.2023.1282768/fullCT-fractional flow reservepericoronary fat attenuation indexwhite matter hyperintensityradiomicsmachine learning |
| spellingShingle | Jie Hou Jie Hou Hui Jin Hui Jin Yongsheng Zhang Yuyun Xu Feng Cui Xue Qin Lu Han Zhongyu Yuan Guangying Zheng Jiaxuan Peng Zhenyu Shu Xiangyang Gong Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study Frontiers in Cardiovascular Medicine CT-fractional flow reserve pericoronary fat attenuation index white matter hyperintensity radiomics machine learning |
| title | Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study |
| title_full | Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study |
| title_fullStr | Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study |
| title_full_unstemmed | Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study |
| title_short | Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study |
| title_sort | hybrid model of ct fractional flow reserve pericoronary fat attenuation index and radiomics for predicting the progression of wmh a dual center pilot study |
| topic | CT-fractional flow reserve pericoronary fat attenuation index white matter hyperintensity radiomics machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1282768/full |
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