The ultimate goal of radiation therapy is to maximize tumor killing with lethal radiation doses while minimizing normal tissue damage during radiation treatment. Although tumor control has been improved significantly with the advent of advanced beam delivery and image-guidance technologies, normal tissue toxicity continues to be of growing concern in the clinic. There are four major reasons: (1) leakage and scatter doses associated with advanced beam delivery are not accurately considered by commercial treatment planning system (TPS) dose calculation methods; (2) TPS dose calculations are only performed for contoured organs within a patient's anatomical volume of interest while providing no dose information for non-contoured organs; (3) organ doses on treatment day can be quite different from planned doses due to changes in organ volume, shape and location; (4) kilo-voltage imaging doses are not considered in total dose accumulation as current commercial TPS cannot simulate kilo-voltage x-rays dose deposition. For these reasons and without warning some patients may accumulate dangerously high doses in radiosensitive organs over time and be susceptible to radiation-related side effects. A number of recent studies have shown that non-negligible second cancer risks are associated with increased organ doses from scatter and leakage radiations that are not correctly accounted for by commercial TPS. In order to achieve maximal benefits of modern radiotherapy with minimal normal tissue toxicities, one must have an accurate and comprehensive account of organ doses for the individual patient. Hence, we propose to develop a personal organ dose archive (PODA) where 3D dose distributions, treatment plans and radiation responses of all the relevant organs are recorded for each individual patient undergoing radiotherapy. Our idea is that through comprehensive tracking and accurate mapping of dose accumulation in all organs we can provide a safety mechanism for early warning and help improve clinical decisions regarding radiation treatment for individual patient, similar to submarine topography detailing the geography of oceans. The goal of this project is to develop a personal organ dose archive based on 3D organ dose tracking and mapping for individual patients over time so that the safety of patients receiving radiation therapy is improved including pre and post care management.
The specific aims of this project are: 1) to develop a graphics processing unit (GPU) based Monte Carlo engine for accurate and fast dose calculations in patients; 2) to build a personal organ dose archive based on dose tracking and deformable image registration; and 3) to assess the effectiveness of the developed personal organ dose archive by tracking 40 patients undergoing radiotherapy in the clinic. We hypothesize that an accurate and comprehensive account of organ doses from both therapy beams and image-guidance procedures may be used to improve patient safety and reduce normal tissue toxicities associated with radiotherapy.
Normal tissue toxicity resulting from cumulative doses of radiation is not uncommon in patients receiving radiotherapy, a form of therapy that millions of patients receive in the United States every year. In this project, we propose to build a novel personal organ dose archive (PODA) to track and accumulate doses from all therapeutic and diagnostic procedures to organs in 3D space for the individual patient undergoing radiotherapy. With PODA we can provide an important safety mechanism to help prevent irreversible radiation damage to normal tissues and provide a comprehensive organ dose database to help clinicians make informed decisions for individual patients including pre and post care management.
| Roffman, David; Hart, Gregory; Girardi, Michael et al. (2018) Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep 8:1701 |
| Ali, Issa; Hart, Gregory R; Gunabushanam, Gowthaman et al. (2018) Lung Nodule Detection via Deep Reinforcement Learning. Front Oncol 8:108 |
| Deng, Jun (2017) Personalized and Conscientious Medical Imaging: To Image or Not to Image. JAMA Oncol 3:443-444 |
| Ming, Xin; Feng, Yuanming; Liu, Ransheng et al. (2017) A measurement-based generalized source model for Monte Carlo dose simulations of CT scans. Phys Med Biol 62:1759-1776 |
| Deng, Jun; Kim, John J (2017) Advances in the Decision to Image or Not to Image-Reply. JAMA Oncol 3:565 |
| Ming, Xin; Feng, Yuanming; Yang, Chengwen et al. (2016) Radiation-induced heart disease in lung cancer radiotherapy: A dosimetric update. Medicine (Baltimore) 95:e5051 |
