(Sunday, 7/14/2019)
Room: ePoster Forums
Purpose: Patients with gynecological malignancies often present with large volumes including para-aortic and inguinal nodes for SIB. Varian LINACs equipped with HDMLC are difficult to treat large targets due to field size limitation (22cm x 40cm). Treatment planning with conventional field matching is not only challenging but also labor intensive. This study investigated the feasibility of autoplan in Pinnacle 9.10 to efficiently design plans deliverable by the HDMLC using a novel step and arc(stepNarc) technique.
Methods: 7 patients previously treated with VMAT using Millennium MLC were retrospectively studied. Dosimetric quality metricss (DQM), i.e., D2cc and the mean dose to the bladder, rectum, bowels and the pelvic bone marrow (PBM) were obtained. Averaged DQM were subsequently used as the only OAR objectives for optimization. Four isocenters longitudinally spread along the PTV were employed with one arc per isocenter. Without further tuning, DQM values were compared with that of clinical plans.
Results: The median PTV length was 27.6cm (19.2-33.3cm). The average±sd for the D2cc bladder, rectum, small bowel, large bowel and mean PBM doses were 48.6±2.9, 47.4±0.5, 50.2±3.8, 50.2±3.8, 29.0±1.7Gy, respectively for the clinical plans. Whereas the corresponding values were 48.0±2.3, 47.3±0.5, 49.6±3.4, 49.0±3.8 and 26.7±0.8Gy for the plans optimized using the autoplanning for stepNarc delivery with HDMLC. The D2cc to the GU/GI OARs were similar; the mean marrow dose was favorably reduced. The DQM variations in the new plans were smaller than the clinical plans demonstrating uniformity in planning process regardless the patient anatomy.
Conclusion: StepNarc VMAT technique combined with autoplanning may provide a novel solution to treat larger volumes using HDMLC efficiently improving clinical workflow. Plans obtained with autoplanning feature in Pinnacle 9.10 using the standardized objectives derived from similar patients exceeded the established clinical quality baseline with less variation for the given patient cohort, while reducing planning effort considerably.
Keywords
Treatment Planning, Optimization
Taxonomy
Not Applicable / None Entered.
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