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Start Over Author "Zhang, Long Jiang" Journal european radiology
188 results on '"Zhang, Long Jiang"'

12. The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study

Author

Di Jiang, Meng, Zhang, Xiao Lei, Liu, Hui, Tang, Chun Xiang, Li, Jian Hua, Wang, Yi Ning, Xu, Peng Peng, Zhou, Chang Sheng, Zhou, Fan, Lu, Meng Jie, Zhang, Jia Yin, Yu, Meng Meng, Hou, Yang, Zheng, Min Wen, Zhang, Bo, Zhang, Dai Min, Yi, Yan, Xu, Lei, Hu, Xiu Hua, Yang, Jian, Lu, Guang Ming, Ni, Qian Qian, and Zhang, Long Jiang

Published
2021
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17. The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography

Author

Xu, Peng Peng, Li, Jian Hua, Zhou, Fan, Jiang, Meng Di, Zhou, Chang Sheng, Lu, Meng Jie, Tang, Chun Xiang, Zhang, Xiao Lei, Yang, Liu, Zhang, Yuan Xiu, Wang, Yi Ning, Zhang, Jia Yin, Yu, Meng Meng, Hou, Yang, Zheng, Min Wen, Zhang, Bo, Zhang, Dai Min, Yi, Yan, Xu, Lei, Hu, Xiu Hua, Liu, Hui, Lu, Guang Ming, Ni, Qian Qian, and Zhang, Long Jiang

Published
2020
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19. Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study

Author

Zhou, Xi Yang, primary, Tang, Chun Xiang, additional, Guo, Ying Kun, additional, Chen, Wen Cui, additional, Guo, Jin Zhou, additional, Ren, Gui Sheng, additional, Li, Xiao, additional, Li, Jun Hao, additional, Lu, Guang Ming, additional, Huang, Xiang Hua, additional, Wang, Yi Ning, additional, Zhang, Long Jiang, additional, and Yang, Gui Fen, additional

Published
2023
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25. The prognostic value of CT-derived fractional flow reserve in coronary artery bypass graft: a retrospective multicenter study

Author

Zu, Zi Yue, primary, Xu, Peng Peng, additional, Chen, Qian, additional, Chen, Yan Chun, additional, Qi, Jian Chen, additional, Tang, Chun Xiang, additional, Zhou, Chang Sheng, additional, Xu, Cheng, additional, Sun, Xin Jie, additional, Lu, Meng Jie, additional, Lu, Guang Ming, additional, Wang, Yi Ning, additional, Xu, Yi, additional, and Zhang, Long Jiang, additional

Published
2022
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37. Correction to: Serial coronary CT angiography–derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease

Author

Yang, Liu, primary, Xu, Peng Peng, additional, Schoepf, U. Joseph, additional, Tesche, Christian, additional, Pillai, Balakrishnan, additional, Savage, Rock H., additional, Tang, Chun Xiang, additional, Zhou, Fan, additional, Wei, Hao Dong, additional, Luo, Zhong Qiang, additional, Wang, Qing Gen, additional, Zhou, Chang Sheng, additional, Lu, Meng Jie, additional, Lu, Guang Ming, additional, and Zhang, Long Jiang, additional

Published
2021
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42. Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease.

Author

Tang, Chun Xiang, Qiao, Hong Yan, Zhang, Xiao Lei, Di Jiang, Meng, Schoepf, U. Joseph, Rudziński, Piotr Nikodem, Giovagnoli, Dominic P., Lu, Meng Jie, Li, Jian Hua, Wang, Yi Ning, Zhang, Jia Yin, Hou, Yang, Zheng, Min Wen, Zhang, Bo, Zhang, Dai Min, Hu, Xiu Hua, Xu, Lei, Liu, Hui, Lu, Guang Ming, and Zhang, Long Jiang

Abstract

Objectives: To propose a novel functional Coronary Artery Disease–Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)–derived fractional flow reserve (FFRCT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD). Methods: Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFRCT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFRCT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model. Results: Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS. Conclusion: The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario. Key Points: • The novel functional CAD-RADS category system with FFRCTintegrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes. [ABSTRACT FROM AUTHOR]

Published
2022
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43. One-year outcomes of CCTA alone versus machine learning–based FFRCT for coronary artery disease: a single-center, prospective study.

Author

Qiao, Hong Yan, Tang, Chun Xiang, Schoepf, U. Joseph, Bayer 2nd, Richard R., Tesche, Christian, Di Jiang, Meng, Yin, Chang Qing, Zhou, Chang Sheng, Zhou, Fan, Lu, Meng Jie, Jiang, Jian Wei, Lu, Guang Ming, Ni, Qian Qian, and Zhang, Long Jiang

Abstract

Objectives: To explore downstream management and outcomes of machine learning (ML)–based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. Methods: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. Results: In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). Conclusion: In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. Key Points: • In stable patients with intermediate stenosis, ML-based FFRCT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRCTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFRCT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease. [ABSTRACT FROM AUTHOR]

Published
2022
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47. The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study

Author

Di Jiang, Meng, primary, Zhang, Xiao Lei, additional, Liu, Hui, additional, Tang, Chun Xiang, additional, Li, Jian Hua, additional, Wang, Yi Ning, additional, Xu, Peng Peng, additional, Zhou, Chang Sheng, additional, Zhou, Fan, additional, Lu, Meng Jie, additional, Zhang, Jia Yin, additional, Yu, Meng Meng, additional, Hou, Yang, additional, Zheng, Min Wen, additional, Zhang, Bo, additional, Zhang, Dai Min, additional, Yi, Yan, additional, Xu, Lei, additional, Hu, Xiu Hua, additional, Yang, Jian, additional, Lu, Guang Ming, additional, Ni, Qian Qian, additional, and Zhang, Long Jiang, additional

Published
2020
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48. Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

Author

Tang, Chun Xiang, Guo, Bang Jun, Schoepf, Joseph U., Bayer II, Richard R., Liu, Chun Yu, Qiao, Hong Yan, Zhou, Fan, Lu, Guang Ming, Zhou, Chang Sheng, and Zhang, Long Jiang

Subjects
PROPORTIONAL hazards models, CORONARY angiography, PROGNOSIS
Abstract

Objectives: To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation. Methods: Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE). Results: Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032–1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006–1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594–0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846. Conclusions: Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients. Key Points: • Machine-learning-based FFRCTis feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCTalong with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCTin patients with moderate-to-high or high risk needs to be further studied. • FFRCTmight refine the clinical pathway of patients with stent implantation to invasive catheterization. [ABSTRACT FROM AUTHOR]

Published
2021
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