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Sunan Cui, PhD

Assistant Professor

Education and Training

College: University of Science and Technology of China, Hefei, China

Graduate School: University of Michigan, Ann Arbor.

Selected Publications from total 11 publications

Cui, S, Pratx, G. 3D computational model of oxygen depletion kinetics in brain vasculature during FLASH RT, and its implications for in vivo oximetry experiments. Med Phys. 2022; 49: 3914– 3925. https://doi.org/10.1002/mp.15642 [Editor’s choice & front cover article]

Sunan Cui, Randall K. Ten Haken, Issam El Naqa. “Integrating multi-omics information in deep learning architectures for joint actuarial outcome prediction in non-small-cell lung cancer patients after radiation therapy,” International Journal of Radiation Oncology· Biology ·
Physics, 2021 July, 110(3).

Cui, S., Tseng, H.-H., Pakela, J., Ten Haken, R.K. and El Naqa, I. (2020), Introduction to machine and deep learning for medical physicists. Med. Phys., 47: e127-e147. https://doi.org/10.1002/mp.14140

Cui, S., Luo, Y., Tseng, H.-H., Ten Haken, R.K. and El Naqa, I. (2019), Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage. Med. Phys., 46: 2497-2511. https://doi.org/10.1002/mp.13497

 

Tseng, H.-H., Luo, Y., Cui, S., Chien, J.-T., Ten Haken, R.K. and Naqa, I.E. (2017), Deep reinforcement learning for automated radiation adaptation in lung cancer. Med. Phys., 44: 6690-6705. https://doi.org/10.1002/mp.12625 [Farrington Daniels Award, best paper of the year in Medical physics]

Research Interests

Deep learning and machine learning in personalized and adaptive RT
Data-driven outcome models
Radiomics and multi-omics
Radiobiological modeling of FLASH RT

 

Clinical interests:

adaptive radiation therapy
automation of treatment planning and QA
Scripting

 

Professional involvement:

Associate editor in editorial board for British Journal of Radiology.