ESTRO 2023 - Abstract Book

S1317

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ESTRO 2023

corresponding manual-plans. PTV_3625 D2 % increased marginally in all cases and visually there were increased regions of 105 % dose within CTV_4000. All target doses remained within the PACE tolerances.

Figure 2: Flow diagram of prostate SABR auto-planning model training (left) and testing (right). All automatically generated treatment plans were reviewed by an expert urology clinical oncologist and were deemed to be clinically acceptable. Conclusion DVH prediction based on OAR subdivision and nominal differential DVHs has been shown to be an effective way of automating prostate SABR treatment planning. As clinics adopt prostate SABR in favour of longer treatment fractionations, this method has the potential to provide a vendor neutral solution to assuring treatment plan quality.

PO-1622 Evaluation of a CT scanner based deep learning auto-contouring solution for lung radiotherapy

M. Williams 1 , S. Berenato 1 , C. Möhler 2 , A. Millin 1 , P. Wheeler 1

1 Velindre Cancer Centre, Radiotherapy Physics, Cardiff, United Kingdom; 2 Siemens Healthineers, ,, Forchheim, Germany

Purpose or Objective For non-small cell lung cancer radiotherapy (NSCLC), geometrically and dosimetrically evaluate DirectORGANS (Version VA30): a commercial AI solution that is natively integrated into a CT scanner and utilises dedicated reconstructions optimised for auto-contouring. Materials and Methods CT scans of 20 NSCLC patients were sequentially selected to evaluate AI contours for lungs (lt & rt), heart, oesophagus (oes) and cord. 3 plan generation ‘pipelines’ were considered; the use of AI generated contours (AI- Std ), full amendment of the AI generated contours (AI -FullEd ), and manual delineation MD- Ob1 . Contouring time was recorded and plans generated for each pipeline using a validated 55Gy in 20# automated planning solution. Contour sets AI- Std and AI- FullEd were geometrically and dosimetrically compared to MD- Ob1 . For dosimetric comparison the error in both Reported Dose (RD) and Patient Dose (PD) was evaluated. RD was defined as DVH parameters that would be reported in patient records for a given pipeline. The dose distribution of each pipeline plan was evaluated on both the reference (RD- Ref ) and pipeline (RD- Pipe ) contour sets, with the difference calculated to assess the impact of contour discrepancies on RD. PD was defined as the best estimate of the actual dose the patient would receive and was extracted from the pipeline plan’s DVH using the MD- Ob1 (PD- Pipe ). By comparing PD- Pipe with plans generated by and evaluated using MD- Ob1 (PD- Ref ), a contour set’s influence on the optimisation process and hence final dose distribution, was assessed. Results Compared to MD- Ob1 , AI- FullEd reduced median delineation time by 50%, 61%, 37%, 28% and 14% for lung_lt, lung_rt, oes, cord and heart respectively. For heart, AI- FullEd increased delineation time in 8 of 20 cases. For lung_lt, lung_rt, oes and cord, AI- Std contours exhibited good geometric alignment to MD- Ob1 with median mean surface distances (MSD) <1.1mm and median DSC results of 0.97, 0.98, 0.80 and 0.88 respectively. For heart, agreement was poorer (MSD=3.4mm, DSC=0.90). AI- FullEd led to small improvements in overall agreement for lung, cord and oes, with moderate improvements for heart (Fig1).