AI Treatment Planner
Treatment planning for cancer radiotherapy, where an optimal treatment strategy is designed for each individual patient and executed for the whole treatment course, is analogous to the design of a blueprint for building construction. If a treatment plan is poorly designed, the desired treatment outcome cannot be achieved, no matter how well other components of radiation therapy are performed. In the current clinical workflow, a treatment planner works towards a good quality plan in a trial-and-error fashion. Many rounds of consultation between the planner and physician are needed to reach a plan of physician’s satisfaction, because physician’s preference for a particular patient can hardly be quantified and precisely conveyed to the planner. Consequently, planning time can be up to a week for complex cases and plan quality may be poor and can vary significantly due to varying levels of physician and planner’s skills and physician-planner cooperation, etc., which substantially deteriorates treatment outcomes.
We believe that AI technologies have a great potential to revolutionize treatment planning. Treatment planning consists of two major aspects: commonality and individuality. By exploiting the commonality through deep supervised learning, we can develop a treatment plan as good as those for previously treated similar patients. The individuality can be actualized by learning physician’s special considerations for a particular patient using deep reinforcement learning. We believe that an AI-based intelligent treatment planning system can consistently produce high-quality treatment plans with extremely high efficiency.
- Barkousaraie, A. S., Ogunmolu, O., Jiang, S., & Nguyen, D. (2019, October). Using supervised learning and guided monte carlo tree search for beam orientation optimization in radiation therapy. In Workshop on Artificial Intelligence in Radiation Therapy (pp. 1-9). Springer, Cham. (journal)
- Ma, J., Bai, T., Nguyen, D., Folkerts, M., Jia, X., Lu, W., ... & Jiang, S. (2019, October). Individualized 3D dose distribution prediction using deep learning. In Workshop on Artificial Intelligence in Radiation Therapy (pp. 110-118). Springer, Cham. (journal)
- Barragán‐Montero, A. M., Nguyen, D., Lu, W., Lin, M., Norouzi‐Kandalan, R., Geets, X., ... & Jiang, S. (2019). Three‐dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations. Medical Physics. (journal) (arXiv)
- Nguyen, D., Jia, X., Sher, D., Lin, M.-H., Iqbal, Z., Liu, H., & Jiang, S. (2019). 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture. Physics in Medicine & Biology, 64(6), 065020. doi:10.1088/1361-6560/ab039b. (journal) (arXiv)
- Nguyen, D., Long, T., Jia, X., Lu, W., Gu, X., Iqbal, Z., & Jiang, S. (2019). A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Scientific Reports, 9(1), 1076. (journal) (arXiv)
- Nguyen, D., McBeth, R., Barkousaraie, A.S., Bohara, G., Shen, C., Jia, X., & Jiang, S. (2019) Incorporating human and learned domain knowledge into training deep neural networks: a differentiable dose volume histogram and adversarial inspired framework for generating pareto optimal dose distributions in radiation therapy. Medical Physics. (journal) (arXiv)
- Xing, Y., Nguyen, D., Lu, W., Yang, M., & Jiang, S. (2019). A feasibility study on deep learning-based radiotherapy dose calculation. Medical Physics. (journal) (arXiv)
- Nguyen, D., Barkousaraie, A. S., Shen, C., Jia, X., & Jiang, S. (2019). Generating pareto optimal dose distributions for radiation therapy treatment planning. Part of the Lecture Notes in Computer Science book series, volume 11769. (journal) (arXiv)
- Barkousaraie, A. S., Ogunmolu, O., Jiang, S., & Nguyen, D. (2019). A fast deep learning approach for beam orientation optimization for prostate cancer IMRT treatments. arXiv preprint arXiv:1905.00523. (arXiv)
- Shen, C., Gonzalez, Y., Klages, P., Qin, N., Jung, H., Chen, L., ... & Jia, X. (2019). Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer. Physics in Medicine & Biology, 64(11), 115013. (journal) (arxiv)