The present proposal aims to develop a rapid learning system for radiation therapy that can provide evidence based, patient-specific treatment guidance for physicians. Rapid-learning health care is a vision proposed by Institute of Medicine to transform the health care delivery into one which generates and applies ?as rapidly as possible the evidence needed to deliver the best care for each cancer patient?. Radiation therapy (RT) is a cancer treatment modality that applies complex radiation delivery equipment to deliver highly conformal dose distribution with minimized damage to organs-at-risk (OARs). Because of the complexity of technologies, the incomplete understanding of radiation effects, and the variability of patients and patient conditions, significant improvements in RT effectiveness can come from learning to use the current RT technologies optimally. In the past a few years, our group and a number of other research groups have developed IMRT dose prediction and planning models using routine clinical plan data that produced encouraging results in learning planning knowledge and improving plan quality. These efforts represent early successes in the first aspect of a rapid learning system. However, these existing efforts have mostly focused on a few major cancer sites and are limited to the ?learning? aspect. Substantial further work on expanding the models, translating the models into clinical practice, and closing the loop for continuous learning is required to truly enable rapid learning in radiation therapy. The present proposal aims to develop a comprehensive and integrated set of models and methods that will enable rapid learning in radiation therapy with the following specific aims: (1) Develop and enhance IMRT planning models to cover all major cancer sites and treatment scenarios; (2) Translate the models into clinical practice to provide best-achievable patient-specific RT planning and enable continuous improvement of the models via incremental learning; (3) Validate the knowledge models and assess the performance and value of the rapid learning framework. While this project will focus on rapid learning of the planning aspect of radiation therapy, we anticipate that the same framework can be extended to incorporate outcomes data and ultimately lead to a complete rapid learning framework that leads to continuously improved quality of cancer care at lower cost.
Radiation therapy (RT) is an effective and broadly used procedure for treating most types of cancer, a major cause of death in the United States and the world. The RT dose prediction models and rapid learning methods developed in this present proposal will integrate multiple knowledge models for radiation therapy planning and provide evidence based, patient-specific best RT planning decisions in clinical practice. These models are developed by systematically learning from clinical data and are readily deployed for clinical implementation once built. Further, this system is built upon rapid learning mechanism such that models are continuously accessing the quality of new practice data and will invoke incremental learning when necessary. We believe the results of this research will lead to better quality of cancer care at lower cost.
|Zhang, Jiahan; Wu, Q Jackie; Xie, Tianyi et al. (2018) An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning. Front Oncol 8:57|
|Sheng, Yang; Ge, Yaorong; Yuan, Lulin et al. (2017) Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. Med Phys 44:5617-5626|