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Reducing Inter-Operator Variability in Prostate VMAT Treatments with a Commercially Available Knowledge-Based Planning Solution

M Fusella

A Scaggion1*, M Fusella1 , M Rossato1 , N Pivato1 , S Bacco1 , A Roggio1 , L Arana Pena2 , M Paiusco1 , (1) Veneto Institute of Oncology IOV - IRCCS, Padua, Italy, Padova, PD, (2) The Abdus Salam International Centre for Theoretical Physics, Trieste, TS


SU-I-GPD-T-376 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To evaluate the potential of a commercial knowledge-based planning (KBP) algorithm to standardize and improve the quality of the radiotherapy treatment. This study evaluates whether the predicted DVH constraints generated by the KBP algorithm can reduce the inter-operator variability thus providing a better standard of quality.

Methods: We build a model for prostate VMAT treatments using Varian RapidPlan with fourtynine previously treated patients. The model was validated through closed-loop and open-loop (ten patients) tests using Plan Quality Metric (PQM) score distributions generated by PlanIQ software (SunNuclear).Further six patients, not included in the training database, were anonymized and randomized. Seven operators, with different planning expertise (ranked on the basis of working years), were asked to manually obtain a clinical VMAT plan (mVMAT) for each of these patients. Subsequently, each operator replied the planning procedure assisted by RapidPlan DVH predictions obtaining a second VMAT plan (rpVMAT). The potential of RapidPlan to reduce the inter-operator variability was evaluated comparing paired plans in terms of PQM score.

Results: mVMAT and rpVMAT plans resulted in similar target coverage while a reduction in OAR sparing variability was seen for rpVMAT plans.Rectum PQM values were higher in rpVMAT for all patients with a reduced variability for five patients out of six. Bladder PQM values were higher in rpVMAT for five out of six patients with a reduced variability for four out of six. Total PQM values were higher in rpVMAT for all patients with a reduced variability for five out of six.

Conclusion: Using RapidPlan predictions reduces the overall inter-planner variability and a general improvement of plans statistics is achieved.This study provides a quantitative measure of the RapidPlan potential as an instrument to improve plan quality with a better standard outcome among operators.

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