ESTRO 37 Abstract book

S1029

ESTRO 37

15 mm for PTV and OARs was used as robustness setting in the anterior and left or right direction depending on the tumor side. To estimate the robustness of the plan, the isocenter was shifted by 5 mm and 10 mm in the posterior and left or right direction. To simulate anatomical change, we used a virtual bolus of 5mm and 10mm setting in the anterior and left/right direction. The resulting perturbed dose was compared to the nominal plan, optimized without RO or bolus. All plans were compared on the basis of dosimetric end-points, target coverage (D95%) and OAR doses. Results

patient, assessed by rectum and target shape and overlap, to allow for developed AP techniques to account for the range of patient variability encountered within the clinic. Once AP techniques were developed, an additional 10 prostate patient datasets were distributed by each centre. Automated treatment planning was performed on all patients by each centre using the developed AP techniques, resulting in a total of 3 plans for 30 patients across three treatment protocols. Plan quality was assessed through DVH analysis and number of protocol compliances. Results The number of deviations for AP techniques from centres A, B, and C were 28 (2 constraint, 1 hard, 12 medium, 13 soft, compliance = 91.5%), 52 (3 constraint, 2 hard, 35 medium, 12 soft, compliance = 87.6%), and 9 (3 constraint, 2 hard, 4 medium, compliance = 96.7%) respectively. Constraint and high priority deviations of host-centre protocols were CTV D99% > 78Gy, rectum V70Gy < 10%, rectum V75Gy < 5% (centre A); rectal wall V75Gy < 10% (centre B); and rectum V74Gy < 1 cc (centre C). Figure 1 shows mean PTV DVH data for AP techniques from centres A (red), B (blue), and C (green) across all datasets. Significantly improved dose distributions for PTV were achieved by centres A and C for centre B patients (dashed), as well as by centre A for centre C patients (dotted). Conversely, figure 2 reveals poorer high dose sparing for rectum due to centre A AP technique.

All the three methods showed good robustness to 5mm shift of the isocenter or with a 5mm anatomical change. In the case of larger changes or offsets, the virtual bolus method seems better than RO and nominal optimization regarding target coverage (p<0,001, t-test).Nominal optimization generates more hot spots than the virtual bolus and RO methods.Regarding OAR, all methods generate the same perturbed dose. Conclusion A virtual bolus and a “robust” optimization methods for breast VMAT plans, introducing a “skin flash” and maximizing the probability of satisfying the planning criteria in the presence of respiratory motion and setup uncertainty, has been evaluated. Only the virtual bolus method maintains target coverage and prevents the increase of hot spots. We observe similar results by calculating on CTs for which we found real anatomical variations. EP-1897 A multi-centre comparison of automated treatment planning for prostate cancer D. Roach 1,2 , C. Rønn Hansen 3,4,5 , G. Wortel 6 , H.R. Jensen 3 , C. Ochoa 7 , E. Damen 6 , P. Vial 2,4,7 , T. Janssen 6 1 University of New South Wales, Faculty of Medicine, Sydney, Australia 2 Ingham Institute for Applied Medical Research, Medical Physics, Liverpool, Australia 3 Odense University Hospital, Laboratory of Radiation Physics, Odense, Denmark 4 University of Sydney, Institute of Medical Physics- School of Physics, Sydney, Australia 5 University of Southern Denmark, Institute of Clinical Research, Odense, Denmark 6 The Netherlands Cancer Institute, Department of Radiation Oncology, Amsterdam, The Netherlands 7 Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Liverpool, Australia Purpose or Objective To investigate if automated treatment planning techniques can be adapted across departments to different local protocols for prostate radiotherapy, providing an assessment of whether such techniques have potential to improve treatment plan quality during future clinical trials. Material and Methods Three treatment centres, two from Europe and one from Australia, and each using Pinnacle’s Autoplanning (AP) software, distributed three prostate patient datasets and local protocols as learning datasets for AP technique development. Centres modified local prostate AP techniques to create protocol compliant AP techniques for each centre. Dose objectives were graded as critical, hard, medium, or soft by the host-centre. The learning datasets each included an easy, medium, and difficult

Figure 1: Mean PTV DVH for all AP techniques across all protocols

Figure 2: Mean rectum DVH for all AP techniques across all protocols Conclusion Automated treatment planning techniques that met each centre’s protocol requirements were successfully developed using a small learning dataset of three patients. Differences in prioritisation of dose objectives affected treatment plan comparison, as centre A techniques produced superior PTV dose coverage, while centre B and C techniques recorded significantly improved high dose sparing for rectum. The study shows that use of AP to implement new protocols across international centres is feasible, and could be utilised to improve plan quality in future clinical trials.