Research

Treatment Planning

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.

 

Schematic of an artificial intelligent radiation therapy planning system.

 

Publications

  1. Kandalan RN, Nguyen D, Rezaeian, Barragan-Montero AM, Breedveld S, Namuduri K, Jiang S, Lin MH. (2020) Dose prediction with deep learning for prostate cancer radiation therapy: model adaptation to different treatment planning practices. (arXiv)

  2. Bohara G, Barkousaraie AS, Jiang S, Nguyen D. (2020) Using deep learning to predict beam-tunable pareto optimal dose distribution for intensity modulated radiation therapy. (arXiv)

  3. Xing Y, Zhang Y, Nguyen D, Lin MH, Weiguo L, Jiang S. (2020) Boosting radiotherapy dose calculation accuracy with deep learning. (arXiv)
  4. Chenyang S, Chen L, Gonzalez Y, Jia X. (2020) Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy. (arXiv)
  5. Barkousaraie AS, Bohara G, Jiang S, Nguyen D. (2020) A reinforcement learning application of guided Monte Carlo tree search algorithm for beam orientation selection in radiation therapy. Med Phys. (arXiv)
  6. Wu C, Nguyen D, Xing Y, Montero AB, Schuemann J, Shang H, Pu Y, Jiang S. (2020) Improving proton dose calculation accuracy by using deep learning. (arXiv)
  7. Barkousaraie AS, Ogunmolu O, Jiang S, Nguyen D. (2020) A fast deep learning approach for beam orientation optimization for prostate cancer IMRT treatments. Med Phys. (arXiv)
  8. Xing Y, Nguyen D, Lu W, Yang M, Jiang S. (2019) A feasibility study on deep learning-based radiotherapy dose calculation. Med Phys(journal) (arXiv)
  9. Sadeghnejad BA, Ogunmolu O, Jiang S, Nguyen D. (2019) A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy. Med Phys. (journal)
  10. Nguyen D, McBeth R, Barkousaraie AS, 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. Med Phys. (journal) (arXiv)
  11. Barkousaraie AS, Ogunmolu O, Jiang S, Nguyen D. (2019) Using supervised learning and guided monte carlo tree search for beam orientation optimization in radiation therapy. In Workshop on Artificial Intelligence in Radiation Therapy. 1-9. Springer, Cham. (journal)
  12. Ma J, Bai T, Nguyen D, Folkerts M, Jia X, Lu W, ...  Jiang S. (2019) Individualized 3D dose distribution prediction using deep learning. In Workshop on Artificial Intelligence in Radiation Therapy. 110-118. Springer, Cham. (journal)
  13. Nguyen D, Barkousaraie AS, 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)
  14. Barragán‐Montero AM, 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. Med Phys. (journal) (arXiv)
  15. 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. Phys Med Biol, 64(11), 115013. (journal) (arXiv)
  16. Nguyen D, Jia X, Sher D, Lin MH, 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. Phys Med Biol, 64(6), 065020. doi:10.1088/1361-6560/ab039b. (journal) (arXiv)
  17. 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. Sci Rep. 9(1), 1076. (journal(arXiv