Program Information

Experience Based Predicition Model For Automated VMAT Planning: A Cervical Cancer Application

z liang¹*, S Qi² , Z Yang³ , Q Li⁴ , (1) Union Hospital, Tongji Medical College, Huazhong University of Science and, Wuhan, HuBei,(2) UCLA School of Medicine, Los Angeles, CA, (3) Union Hospital, Tongji Medical College, Huazhong University of Science and, Wuhan, HuBei, (4) Union Hosptial, Tongji Medical College, Huazhong University of Science and, Wuhan, HuBei

Presentations

SU-E-T-177 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:To develop a planning prediction model based on patient geometry and dose-volume endpoins from previously treated cases; to devise an automated volumetric modulated arc therapy (VMAT) planning for future cases.

Methods:Thirty cervical patients were included for this retrospective study, including22 patients fortraining cohort and 8 for testing cohort. For each patient in the training cohort, a VMAT plan with two full arcs weregenerated using a clinical plan template. The relative volume of the selected organ-at-risks (OARs) within a specified margin of the PTV(Lx) were extracted from overlap volume histograms (OVHs). A prediction model at 2D dose-distance (Lx, Dx) grid were established using a linear regression model. For the testing cohort,the model predicted DVH endpoints were used as constraints to automatically generate a new VMAT plan, the new plans were evaluated against the original plans.

Results:On average, the prediction doses of rectum, bladder and bowel were 1.13Gy, 2.09Gy, 0.81Gy lower than the manual VMAT plans at predicted DVH endpoints. The auto plan showed slightly lower PTV conformity,CI = 1.18(auto) vs CI =1.12(manual) and almost the same PTV uniformity (UI= 0.17(auto) vs UI=0.18(manual)). V40 of rectum and bladder and V30 of bowelfrom auto plan were reduced by 2.36%, 13.56% and 4.36%, respectively. Mean doses of the rectum, bladder and bowel reduced 0.35Gy, 2.17Gy and 1.08Gy, respectively, as compard with the manual plans.

Conclusion:The experience-based prediction model has demonstrated the ability as a plan quality control tool to further improve OARs dose constrains setting in optimization process, butoffers a potential method for generating automatic VMAT plans which significantly improve theplanqualityand planning efficiency.

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