Room: ePoster Forums
Purpose: To propose a dose prediction oriented hybrid effective dose and volume based optimization for knowledge based treatment planning for intensity modulated radiotherapy.
Methods: On the basis of our in-house developed IMRT treatment plan 3-denmensional dose distribution prediction model, we customized a novel hybrid effective dose and volume based optimization engine to further reduce the organs at risk (OARs) dose. The effective dose was oriented by the predicted doses and calculated by using the generalized equivalent uniform dose function in the MatRad platform. Similarly, the effective volume was calculated by the predicted dose distribution. We collected 5 patient cases of head and neck 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 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 and dose statistics.
Results: Both 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 Dmax was effectively reduced, and HI decreased by 0.1837~1.1425, for our proposed optimized method respectively. For Dmean and D50 of the right Parotid gland, the dose decreased by 10.79~12.10Gy and 7.74~12.55Gy, respectively. And for Dmean and D50 of the left parotid gland could be maintained at the same level as the original planned. It is shown that our method is effective for plan improvement.
Conclusion: We proposed a novel dose prediction oriented hybrid effective dose and volume based optimization for knowledge based treatment planning for intensity modulated radiotherapy. This method makes the optimization and evaluation of radiotherapy planning more clinical and biological significance.
Funding Support, Disclosures, and Conflict of Interest: 1) National Key R&D Program of China (NO.2017YFC0113203); 2) National Natural Science Foundation of China (NO.81571771 and 81601577); 3) Post-doctoral Science Foundation of China (NO.2016M592510); 4) Public Welfare Research and Capacity Building Special Foundation of Guangdong, China (2015B020214002)