ESTRO 2020 Abstract book

S862 ESTRO 2020

Table 1

p value Clinca l Plan vs Rapid plan

P value Clinic al Plan vs Hybri d Plan

Clinical plan

Rapid Plan

Hybrid

Parameter

Plan

PTV45V42.75G y[%] ITV45V42.75G y[%] Help contour max Bowel V40 Gy (cc) Bowel V30 Gy (cc) Bowel V15 Gy (cc) Bladder V40Gy (%) Bladder V30 Gy (%) Rectum V40Gy (%) Rectum V30Gy (%)

96.8 (1.2) 99.8 (0.3) 102.1 (1.1) 187 (164)

97.1 (2.0) 99.8 (0.3)

96.4 (0.5) 99.9 (0.2)

[1] Physics and Imaging in Radiation Oncology, 10, 41-48. doi: 10.1016/j.phro.2019.04.005 PO-1505 Knowledge based treatment planning and validation of VMAT for Cervical Cancer. J. Swamidas 1 , S. Pradhan 1 , S. Panda 1 , S. Chopra 1 , A. Mangaj 1 , U. Mahantshetty 2 1 Advanced Centre for Treatment Research and Education in Cancer ACTREC- Tata Memoral Centre, Radiation Oncology, Mumbai, India ; 2 Tata Memorial Hospital- Tata Memorial Centre, Radiation Oncology, Mumbai, India Purpose or Objective To report our experience of knowledge based planning and its validation for VMAT for Cervical Cancer. Material and Methods 30 patients previously treated as part of Image guided intensity modulated E xternal beam radio-chemotherapy and M RI based adaptive BRA chytherapy in locally advanced CE rvical cancer (EMBRACE-II) protocol were used to build a model using knowledge-based planning module (RapidPlan v13.5.35, Eclipse v13.5,Varian Medical Systems). Plan geometry consists of two coplanar arcs of 360 ˚ , collimator angle of 5 ˚ or 355 ˚ , and field size 16x35cm 2 . Dose prescription to PTV pelvis was 45Gy/25fractions. 10 patients from the same clinical trial were randomly chosen to validate this model. A total of three plans were generated: Clinical plan (CP) made by an experienced planner manually, automatic Rapid Plan(RP), based on the model from single optimization without any manual tweaking, and Hybrid Plan(HP), combination of CP and AP. HP plans were generated from the RP, followed by minimal manual tweaking of organ priorities and dose constraints during optimization interactively by the planner. Dose volume parameters for PTV, ITV, and OARs were statistically analysed using paired t test and Wilcoxon signed test Results Out of the three plans HP was found to be superior in terms of organ sparing (bowel V 30Gy ,V 40Gy , and V 15Gy p = 0.001, bladder V 30Gy (%) and V 40Gy (%) p = 0.001) and conformality (V 43Gy /PTV vol and V 36Gy /PTV vol p=0.001) while maintaining target coverage. Table 1, lists the detailed DVH parameters for all the plans and various DVH parameters. However, no significant difference was observed between CP and RP. This confirms that the model is robust, efficient and equivalent to CP, however, a further improvement was observed when the model parameters were tweaked by the planner during optimization. This explains that the model is the average plan model, while, the manual tweaking takes into account the patient specific changes.

0.33 0.8

0.57 0.48

102.2(0. 5) 172(104 ) 374.3(1 74) 1426(42 7)

101.9(0. 4) 161(123 ) 342.5(1 91) 1212(38 9)

0.76 0.13

0.24 0.000

404.4(2 61)

0.45 0.000

1293 (413)

0.2 0.000

49(15) 50.0(12) 45(15) 0.64 0.000

72(11) 74(9)

61(13) 0.34 0.000

83(17) 78(9)

79(13) 0.23 0.01

95(7)

95(5)

93(7)

1.0 0.23

1.06(0.0 4) 1.56(0.0 7)

1.03(0.0 1) 1.50(0.0 6)

0.45 0.000

V43 / VPTV45 1.05(0.0 5) V36 / VPTV 45 1.55(0.1 2)

0.92 0.000

Conclusion Knowledge based planning model was created and validated for VMAT cervical cancer. It was observed that the HP resulted in superior sparing for bowel & bladder and conformality, while maintaining the target coverage. It was found that the rapid plan model was robust and hence will be used clinically in the future. PO-1506 An automated knowledge based treatment planning solution for prostate VMAT J. Wood 1 , M. Aznar 2 , P. Whitehurst 1 1 The Christie NHS Foundation Trust, Christie Medical Physics & Engineering, Manchester, United Kingdom ; 2 The University of Manchester, Radiotherapy Related Research, Manchester, United Kingdom Purpose or Objective This work aimed to develop a novel approach to knowledge based (KB) prostate VMAT treatment planning. Material and Methods Converting well defined PTV dose criteria into optimisation objectives is relatively straightforward. OARs, however, present a greater challenge because they sit in steep dose gradients and their size, shape and position relative to PTVs vary between patients. Precisely predicting optimal OAR DVH parameters is therefore difficult and uncertainties in the predictions ultimately become manifest in the degree to which treatment plans are dosimetrically optimal. The optimal PTV-OAR dose gradient (i.e. dose fall-off per unit distance) is characterised primarily by delivery machine parameters and not patient anatomy. On this basis, a model of the ideal prostate treatment plan was developed – see Figure 1, where the colour gradients represent achievable PTV-OAR dose gradients.