Abstract

Info

Title:: Automatic planning of radiotherapy for cervical carcinoma based on dose prediction

Author(s):: HUANG Shixiong; YANG Songhua; WANG Liang; YU Gongyi; NI Qianxi; Department of Radiation Physics and Technology, Hunan Cancer Hospital, Changsha 410013, China

Keywords:: cervical cancer overlap volume histogram machine learning automatic planning Python Monaco

Abstract:: Objective To establish a regressionmodel for cervical cancer planning dose prediction usingmachine learningmethod, and to realize the automatic planning of radiotherapy for cervical cancer by guiding the predicted dose to generate an optimization template file that can be called byMonaco treatment planning system (TPS).Methods The geometric characteristic values based on overlap volume histogram and the dose target values based on dose volume histogram of organs-at-risk in the postoperative intensity-modulated radiotherapy plans of 50 cervical cancer patients were collected.Aftermodeling, the dose predicted by themodel was automatically generated into aMonaco TPS template file which was then optimized and called by TPS. The proposedmethod was used for automatic planning in 10 test cases that did not participate inmodel training, and the obtained plans were then compared with themanually designed plans. Results Compared with those ofmanual planning, the average design time of automatic planning was reduced by 40min (P<0.05), and there were 3 optimization times less in automatic planning (P<0.05). No significant difference was found in dosimetry indexes and plan execution efficiency (P>0.05). Conclusion The automatic planning of radiotherapy for cervical cancer based on dose prediction canmeet clinical requirements and improve planning efficiency.