Senior Research Associate
Biomedical Engineering
Email: Z Teng Tel.: + 44 (0)1223 762772
Profile/Research
Zhongzhao Teng, PhD, is Senior Research Associate at Department of Radiology, University of Cambridge. He received his BSc and PhD from the Department of Mechanics and Engineering Science, Fudan University, Shanghai, China, in 1998 and 2003, respectively. After the graduation, he joined the Department of Mechanical Engineering, University of Zaragoza, Spain, as a post-doc and further moved to Worcester Polytechnic Institute, MA, USA, to be a research assistant professor in 2007. In 2009, Dr. Teng joined the University Department of Radiology, University of Cambridge and now he is a senior research associate. In 2014, he completed his 2nd doctoral training at Department of Radiology from University of Cambridge. Dr Teng is mainly interested in the translational application of combination of non/mini-invasive medical imaging, such as magnetic resonance imaging (MRI), intravascular ultrasound (IVUS) and optical coherence tomography (OCT) and biomechanical analysis in assessing the vulnerability of atherosclerotic and aneurysmal lesions. Dr Teng is also interested the material behaviour of atherosclerotic and aneurysmal tissues.
Dr Teng’s long-term research plan is focusing on
- The translational application of combination of in vivo medical imaging and mechanical analysis to assess the vulnerability of atherosclerotic lesions; and
- The mechanism of initiation and progression of atherosclerotic lesions due to “malfunctioning” haemodynamics.
Multidisciplinary collaborations between clinicians and non-clinicians have been developed for this long-term aim.
Current Research
Fluid-structure interaction simulation with diseased artery; Mechanism of atherosclerotic plaque progression and rupture; Material behaviour of biological tissue; Collapse behaviour and respiratory dynamics in airway.
Last 5 Publications
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https://radiology.medschl.cam.ac.uk/wp-content/plugins/zotpress/
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22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Caixia%22%2C%22lastName%22%3A%22Fu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jing%22%2C%22lastName%22%3A%22Ding%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Daqiao%22%2C%22lastName%22%3A%22Guo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Zhongzhao%22%2C%22lastName%22%3A%22Teng%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jiang%22%2C%22lastName%22%3A%22Lin%22%7D%5D%2C%22abstractNote%22%3A%22ObjectivesCarotid%20artery%20stenting%20%28CAS%29%20is%20an%20established%20treatment%20for%20local%20stenosis.%20The%20most%20common%20complication%20is%20new%20ipsilateral%20ischemic%20lesions%20%28NIILs%29.%20This%20study%20aimed%20to%20develop%20models%20considering%20lesion%20morphological%20and%20compositional%20features%2C%20and%20radiomics%20to%20predict%20NIILs.Materials%20and%20methodsOne%20hundred%20and%20forty-six%20patients%20who%20underwent%20brain%20MRI%20and%20high-resolution%20vessel%20wall%20MR%20imaging%20%28hrVWI%29%20before%20and%20after%20CAS%20were%20retrospectively%20recruited.%20Lumen%20and%20outer%20wall%20boundaries%20were%20segmented%20on%20hrVWI%20as%20well%20as%20atherosclerotic%20components.%20A%20traditional%20model%20was%20constructed%20with%20patient%20clinical%20information%2C%20and%20lesion%20morphological%20and%20compositional%20features.%20Least%20absolute%20shrinkage%20and%20selection%20operator%20algorithm%20was%20performed%20to%20determine%20key%20radiomics%20features%20for%20reconstructing%20a%20radiomics%20model.%20The%20model%20in%20predicting%20NIILs%20was%20trained%20and%20its%20performance%20was%20tested.ResultsSixty-one%20patients%20were%20NIIL-positive%20and%20eighty-five%20negative.%20Volume%20percentage%20of%20intraplaque%20hemorrhage%20%28IPH%29%20and%20patients%27%20clinical%20presentation%20%28symptomatic%5C%2Fasymptomatic%29%20were%20risk%20factors%20of%20NIILs.%20The%20traditional%20model%20considering%20these%20two%20features%20achieved%20an%20area%20under%20the%20curve%20%28AUC%29%20of%200.778%20and%200.777%20in%20the%20training%20and%20test%20cohorts%2C%20respectively.%20Twenty-two%20key%20radiomics%20features%20were%20identified%20and%20the%20model%20based%20on%20these%20features%20achieved%20an%20AUC%20of%200.885%20and%200.801%20in%20the%20two%20cohorts.%20The%20AUCs%20of%20the%20combined%20model%20considering%20IPH%20volume%20percentage%2C%20clinical%20presentation%2C%20and%20radiomics%20features%20were%200.893%20and%200.842%20in%20the%20training%20and%20test%20cohort%20respectively.ConclusionsCompared%20with%20traditional%20features%20%28clinical%20and%20compositional%20features%29%2C%20the%20combination%20of%20traditional%20and%20radiomics%20features%20improved%20the%20power%20in%20predicting%20NIILs%20after%20CAS.Key%20points%5Cu2022%20Volume%20percentage%20of%20IPH%20and%20symptomatic%20events%20were%20independent%20risk%20factors%20of%20new%20ipsilateral%20ischemic%20lesions%20%28NIILs%29.%20%5Cu2022%20Radiomics%20features%20derived%20from%20carotid%20artery%20high-resolution%20vessel%20wall%20imaging%20had%20great%20potential%20in%20predicting%20NIILs%20after%20CAS.%20%5Cu2022%20The%20combination%20model%20with%20radiomics%20and%20traditional%20features%20further%20improved%20the%20diagnostic%20performance%20than%20traditional%20features%20alone.%22%2C%22date%22%3A%222022-12-01%22%2C%22language%22%3A%22eng%22%2C%22DOI%22%3A%2210.1007%5C%2Fs00330-022-09302-4%22%2C%22ISSN%22%3A%221432-1084%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1007%5C%2Fs00330-022-09302-4%22%2C%22collections%22%3A%5B%224956CJHC%22%2C%22U8HZC85J%22%5D%2C%22dateModified%22%3A%222023-04-06T10%3A36%3A21Z%22%7D%7D%5D%7D
Zhou, D., Wang, Y., Li, S., Wu, W., Sun, X., Zhuang, B., He, J., Xu, J., Yang, W., Zhu, L., Sirajuddin, A., Teng, Z., Zhao, S., & Lu, M. (2023). Ventricular-atrial coupling in subjects with normal, preserved, and reduced left ventricular ejection fraction: insights from cardiac magnetic resonance imaging. European Radiology. https://doi.org/10.1007/s00330-023-09801-y
Gu, S. Z., Huang, Y., Costopoulos, C., Jessney, B., Bourantas, C., Teng, Z., Losdat, S., Maehara, A., Räber, L., Stone, G. W., & Bennett, M. R. (2023). Heterogeneous plaque-lumen geometry is associated with major adverse cardiovascular events. European Heart Journal Open, 3(3), oead038. https://doi.org/10.1093/ehjopen/oead038
Xie, S., Ran, Y., Wang, X., Zhang, Y., Fu, Q., Ren, Y., Liu, J., Teng, Z., & Cheng, J. (2023). Diagnostic potential of routine brain MRI and high-resolution, multi-contrast vessel wall imaging in the detection of internal carotid artery dissection. Frontiers in Neurology, 14, 1165453. https://doi.org/10.3389/fneur.2023.1165453
Tang, H., Zhang, Z., He, Y., Shen, J., Zheng, J., Gao, W., Sadat, U., Wang, M., Wang, Y., Ji, X., Chen, Y., & Teng, Z. (2023). Automatic classification and segmentation of atherosclerotic plaques in the intravascular optical coherence tomography (IVOCT). Biomedical Signal Processing and Control, 85. Scopus. https://doi.org/10.1016/j.bspc.2023.104888
Zhang, R., Zhang, Q., Ji, A., Lv, P., Acosta-Cabronero, J., Fu, C., Ding, J., Guo, D., Teng, Z., & Lin, J. (2022). Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis. European Radiology. https://doi.org/10.1007/s00330-022-09302-4
