Room: Karl Dean Ballroom A1
Purpose: To evaluate the feasibility of using the knowledge-based treatment planning technique to automate treatment planning for total marrow irradiation (TMI) with volumetric modulated arc therapy (VMAT) fields, by using historical helical tomotherapy (HT) TMI plans as input data for dosimetric model construction.
Methods: We retrospectively retrieved 30 clinical HT TMI treatment plans from an institutional clinical trial with uniform prescription dose of 12 Gy to skeletal bones, lymph nodes, spleen, and spinal canal, for 14 male and 16 female patients. Dosimetric data for all the structures were extracted to a commercial knowledge-based planning system for construction of dosimetric models for 4 PTVs and 25 organs at risk (OARs). Structure-specific objectives generated from the dosimetric models were used to optimize five VMAT plans. Each VMAT plan included 8 full arc fields with four isocenters using 6-MV photon beams.
Results: DVH estimation models for 3 PTVs and 19 OARs were constructed and properly trained. In the VMAT-based TMI plans, all the PTVs received comparable dose coverage as in the HT plans. In the 19 OARs with constructed dosimetric models, 11 showed lower average median dose (D50) compared to the HT plans, and 8 shower higher average D50 compared to the HT plans; the difference was not statistically significantly (p > 0.05 in t-tests) except for 2 OARs. The average median dose was 5.7±0.3 Gy and 5.5±0.1 Gy for the left and right lungs, respectively, in the VMAT plans.
Conclusion: This study demonstrated that with the knowledge-based treatment planning technique, prior treatment planning experience and dosimetric data from historical HT plans could be used to facilitate treatment planning for TMI on conventional linacs by automating the optimization process and help achieve high plan quality. The dosimetric models can be made available to aid TMI planning in centers with limited clinical experience.
Treatment Planning, Optimization, Modeling
TH- External beam- photons: VMAT dose optimization algorithms