Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study - Cilla, Savino; Romano, Carmela; Morabito, Vittoria E.; Macchia, Gabriella; Buwenge, Milly; Dinapoli, Nicola; Indovina, Luca; Strigari, Lidia; Morganti, Alessio G.; Valentini, Vincenzo; Deodato, Francesco (Frontiers in Oncology, 2021)
Background: In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle3 for full planning automation of VMAT prostate cancer treatments. Material and Methods: Thirteen low-risk prostate and thirteen high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms using a wishlist to formulate the planning goals and an iterative approach able to mimic the planning procedure adopted by experienced planners. The new Personalized module integrates the Feasibility engine able to supply an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically manually-generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans. Results: Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder and femurs. On average, Pers plans decreased rectal mean dose by 11.3Gy and 8.3Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3Gy and 7.6Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11-16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 minutes for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm -analysis for dose verification. Conclusions: The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.