Qiuwen Wu, PhD

Professor of Radiation Oncology
Address: 00568 Duke South White Zone
Durham, NC 27710
Phone: (919) 613-6727
Email:

Research Interests

My research interests include intensity-modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), electron arc therapy (EAT) and image-guided radiation therapy (IGRT). For IMRT, my work include the development of the research platform, fast and accurate dose calculations, optimization based on physical and biological objectives such as generalized equivalent uniform dose (gEUD), and delivery with dynamic multi-leaf collimator (DMLC). For VMAT, I am interested in the optimization, quality assurance and novel applications. For EAT, I'm interested in the treatment planning and delivery verifications. For IGRT, my work include development of the infrastructure of the online and offline image guidance, characterization of patient anatomic changes and treatment uncertainties, margin calculations, and adaptive treatment planning.

My clinical interests include prostate cancer, head and neck cancer, breast cancer and lung cancer.

Publications

Wang, Wentao, et al. “Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy.Phys Med Biol, vol. 66, no. 24, 2021. Pubmed, doi:10.1088/1361-6560/ac3c14.

Sheng, Yang, et al. “Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.Quant Imaging Med Surg, vol. 11, no. 12, Dec. 2021, pp. 4859–80. Pubmed, doi:10.21037/qims-21-208.

Hito, Martin, et al. “Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings-a phantom simulation approach.Quant Imaging Med Surg, vol. 11, no. 12, Dec. 2021, pp. 4835–46. Pubmed, doi:10.21037/qims-21-51.

Li, Xinyi, et al. “Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning.Phys Med Biol, vol. 66, no. 23, Nov. 2021. Pubmed, doi:10.1088/1361-6560/ac3841.

Stephens, Hunter, et al. “Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization.Biomed Phys Eng Express, vol. 8, no. 1, Nov. 2021. Pubmed, doi:10.1088/2057-1976/ac35f8.

Liu, Bo, et al. “Technical note: A fast and accurate analytical dose calculation algorithm for 125 I seed-loaded stent applications.Med Phys, vol. 48, no. 11, Nov. 2021, pp. 7493–503. Pubmed, doi:10.1002/mp.15207.

Li, X., et al. “Collect Insights of an H&N IMRT Planning AI Agent Through Analyzing Relationships Between Fluence Map Prediction Error and the Corresponding Dosimetric Impacts.International Journal of Radiation Oncology, Biology, Physics, vol. 111, no. 3S, 2021, p. e94. Epmc, doi:10.1016/j.ijrobp.2021.07.479.

Stephens, H., et al. “The Reduction of Computational Cost by Introducing Kernel Sparsity and Truncation Into IMRT Optimization.International Journal of Radiation Oncology, Biology, Physics, vol. 111, no. 3S, 2021, p. e145. Epmc, doi:10.1016/j.ijrobp.2021.07.596.