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Program Information

Development and Clinical Introduction of Automated Radiotherapy Treatment Planning for Prostate Cancer

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D Winkel

D Winkel*, GH Bol, B van Asselen, J Hes, V Scholten, LGW Kerkmeijer, BW Raaymakers, University Medical Center Utrecht, Utrecht, The Netherlands


SU-G-TeP1-5 (Sunday, July 31, 2016) 4:00 PM - 4:30 PM Room: ePoster Theater

Purpose: To develop an automated radiotherapy treatment planning and optimization workflow for prostate cancer in order to generate clinical treatment plans.

Methods: A fully automated radiotherapy treatment planning and optimization workflow was developed based on the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). To evaluate our method, a retrospective planning study (n=100) was performed on patients treated for prostate cancer with 5 field intensity modulated radiotherapy, receiving a dose of 35x2Gy to the prostate and vesicles and a simultaneous integrated boost of 35x0.2Gy to the prostate only. A comparison was made between the dosimetric values of the automatically and manually generated plans. Operator time to generate a plan and plan efficiency was measured.

Results: A comparison of the dosimetric values show that automatically generated plans yield more beneficial dosimetric values. In automatic plans reductions of 43% in the V72Gy of the rectum and 13% in the V72Gy of the bladder are observed when compared to the manually generated plans. Smaller variance in dosimetric values is seen, i.e. the intra- and interplanner variability is decreased. For 97% of the automatically generated plans and 86% of the clinical plans all criteria for target coverage and organs at risk constraints are met. The amount of plan segments and monitor units is reduced by 13% and 9% respectively. Automated planning requires less than one minute of operator time compared to over an hour for manual planning.

Conclusion: The automatically generated plans are highly suitable for clinical use. The plans have less variance and a large gain in time efficiency has been achieved. Currently, a pilot study is performed, comparing the preference of the clinician and clinical physicist for the automatic versus manual plan. Future work will include expanding our automated treatment planning method to other tumor sites and develop other automated radiotherapy workflows.

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