Quality Evaluation of An Automatic VMAT Planning Method for Head and Neck Cancer Cases
L Yuan1*, T Pang2, Y Ge3, T Li4, Y Jiang5, F Yin6, Q Jackie Wu7, (1) Duke University Medical Center, Durham, NC, (2) Peking Union Medical College Hospital, Beijing, Beijing, (3) University of North Carolina at Charlotte, Charlotte, North Carolina, (4) Duke University Medical Ctr., Durham, NC, (5) Peking University 3rd Hospital, Beijing, Beijing, (6) Duke University Medical Center, Durham, NC, (7) Duke University Medical Center, Durham, NCSU-E-CAMPUS-T-5 Sunday 3:00PM - 6:00PM Room: Exhibit Hall
Purpose: To evaluate the dosimetric quality of an automatic volumetric-modulated arc therapy (VMAT) planning method for Head and Neck (HN) cancer cases.
Methods: Eighteen HN cancer cases are planned automatically by 2-arc VMAT. An OAR DVH prediction model automatically generates OAR dose sparing objectives for each patient case based on the patient's anatomical features. The model is trained by prior clinical IMRT plans. In addition, a pre-determined planning template was used for all the cases which specify the arc angles, collimator angles, jaw setting, etc, as well as the optimization priority for each objective, based on past VMAT planning experience. The automatic VMAT planning process requires only one round of optimization for each case. The dosimetric parameters for each OAR and PTV from the VMAT plans are compared with those in clinical IMRT plans.
Results: The means (standard deviation in parenthesis) of the dosimetric parameters in the automatically generated VMAT and the clinical IMRT plans are (in Gy): parotid D50%: VMAT: 25.8 (8.6), IMRT: 25.7 (12.8), oral cavity D50%: VMAT: 33.2 (8.5), IMRT: 31.6 (9.5), larynx D50%: VMAT: 27.6 (13.3), IMRT: 26.3 (14.5), spinal cord + 5mm D2%: VMAT: 34.9 (2.2), IMRT: 36.2 (4.5), Brainstem D2%: VMAT: 18.9 (5.9), IMRT: 20.7(5.9). The dosimetric parameters for the OARs in the VMAT and IMRT plans have no significant differences. The only statistically significant difference is the dose homogeneity of the boost PTV (p = 0.0006), which is 5.2 Gy in VMAT plans vs. 7.1 Gy in IMRT plans.
Conclusion: The automatic VMAT planning method can generate plans with dosimetric quality comparable to clinical IMRT plans generated by an expert. This study suggests the DVH prediction models learned from IMRT planning experience can be utilized to generate dose sparing objectives for VMAT planning.
Funding Support, Disclosures, and Conflict of Interest: Partly supported by NIH/NCI and a Varian master research grant.