Room: AAPM ePoster Library
Purpose: propose a dose prediction oriented equivalent volume reconstruction by radiobiology optimization for knowledge based treatment planning for intensity modulated radiotherapy.
Methods: the basis of our in-house developed IMRT treatment plan 3D dose distribution prediction model, we customized a novel optimization engine based on the radiobiological equivalent of the conventional dose-volume approach to further reduce the OARs dose. We used the tolerated dose of OARs as the reference dose and the corresponding volume at the predicted dose as the reference volume, then added them to the proposed biological optimization model of reconstructed volume for equivalent volume calculation and planning optimization. We collected 4 patient cases of head and neck cancer and 4 of prostate cancer which had received radiotherapy to evaluate the developed method. The target area was optimized by dose-volume-based objective function, and the bilateral parotid glands and bladders were optimized using our new method. The plan quality was investigated by comparing the original plan to the optimized plan in terms of ROI's DVH.
Results: DVH and dose distribution results show comparable target coverage and improved OAR sparing of our proposed optimized plan in comparison with original plans. For all PTVs, D98 remained at a comparable level to the original plan while D??? was effectively reduced for our proposed optimized method. For head and neck cases, D???n and D50 of the bilateral Parotid gland, the dose decreased by 8.69Gy and 9.78Gy, respectively. And for prostate cases, D???n and V10 of the bladder decreased by 3.5Gy and 29%, respectively. It is shown that our method is effective for plan improvement.
Conclusion: proposed a novel dose prediction oriented radiobiological equivalent volume reconstruction based optimization for knowledge based treatment planning for intensity modulated radiotherapy, which makes the optimization and evaluation of radiotherapy planning more clinical and biological significance.
Funding Support, Disclosures, and Conflict of Interest: The present work was supported by the Natural Science Foundation of Guangdong Province, China (No. 2018A0303100020), National Key R&D Program of China (Grant NO. 2017YFC0113203) and the National Natural Science Foundation of China (Grant NO. 11805292, 81601577and 81571771).