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sunan cui

Assistant professor of Medical physics

University of Washington

United States

Research Interests

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About

Dr. Sunan Cui is an Assistant Professor of Medical Physics at the University of Washington, USA. His research primarily focuses on the application of artificial intelligence and machine learning in oncology, particularly in precision medicine and radiation therapy. Recent publications include work on dosimetric analysis for stereotactic ablative radiotherapy and the development of computational models in radiation oncology.

Articles (11)

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2026

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2021

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2020

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2020

Collaborators (4)

Dipesh Niraula

Applied Research Scientist II

H Lee Moffitt Cancer Center and Research Institute

UNITED STATES

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Paul Kinahan

Professor

University of Washington

UNITED STATES

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John Kang

assistant professor

University of Washington

UNITED STATES

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Jie Fu

Assistant Professor

University of Washington

UNITED STATES

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sunan cui

Research Interests

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Articles (11)

Mature Outcomes and Patterns of Failure in the Phase II FLARE-RT Trial of Biological Image-Guided and Risk-Adaptive Chemoradiation for Unresectable NSCLC

Chest wall pain after single-fraction thoracic stereotactic ablative Radiotherapy: Dosimetric analysis from the iSABR trial

A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration

Current status and future developments in predicting outcomes in radiation oncology

Lung Subregion Partitioning by Incremental Dose Intervals Improves Omics-Based Prediction for Acute Radiation Pneumonitis in Non-Small-Cell Lung Cancer Patients

Artificial Intelligence for Outcome Modeling in Radiotherapy

3D computational model of oxygen depletion kinetics in brain vasculature during FLASH RT and its implications for in vivo oximetry experiments

Integrating multi-omics information in deep learning architectures for joint actuarial outcome prediction in non-small-cell lung cancer patients after radiation therapy.

Introduction to machine and deep learning for medical physicists

Machine Learning and Imaging Informatics in Oncology

Collaborators (4)

Dipesh Niraula

Paul Kinahan

John Kang

Jie Fu

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