ESTRO 2020 Abstract Book

S780 ESTRO 2020

Conclusion In this study, we demonstrate that clinical machine learning plans for oropharyngeal cancer patients have comparable plan quality to the reference plans, while more consistent and much more efficient to generate. In future work, we will perform a blinded prospective clinical study on MLO planning versus manual planning. MLO planning can be also be used for automated generation of multiple treatment plans, for both photons and protons. PO-1460 Pre Treatment Patient QA results prediction using deep learning, based on bayesian classification. R. Oozeer 1 , J. Nigoul 2 , N. Bizot 2 , M. Agelou 3 , E. Barat 3 1 Radiation Therapy Consulting, Research Department, Marseills, France ; 2 APHM - La Timone, Radiotherapy, Marseille, France ; 3 CEA List, Laboratoire Modélisation- Simulation et Systèmes, Gif-Sur-Yvette, France Purpose or Objective The evolution of radiation therapy treatments sees the increasing use of VMAT techniques, with often small and very irregular fields, in order to generate highly modulated treatments. Therefore systematic pre treatment QA for each beam is needed to ensure that calculated and measured dose are within tolerance, The objective of this study is to use metrics to quantify the complexity of a treatment plan in order to assess the relevance of the QA. Material and Methods Sixteen complexity metrics have been have been identified as relevant: fifteen from literature (PIMV, AI, MCS, MI…) and one novel (wavelet transform of the fluence map). They can be classified into two categories: the metrics based on geometrical aspects (shapes of the fields, opening of the leaves), and based on fluence. Their calculation use dicom RT files generates by TPS (treatment planning systems). We have used machine learning techniques to create a model linking the Patient QA results and to complexity metrics. Two types of models have been created (pass/fail prediction, based on nonparametric bayesian method) and gamma-index features (passing rate, mean gamma, max gamma, based on regression). Prediction uncertainty has been implemented with a prediction model assessment using the leave-one-out method LOOCV (logarithmic, zero/one and R² scores) and the leave-pair-out method LPOCV (ROC curves and AUC). We have built a prediction model, at APHM – La Timone, France, based on the following equipment ( Elekta Synergy, Beam Modulator, Delta4, Pinnacle3, and different tumor sites (pelvis, prostate, H&N, thorax, brain, …)). 445 VMAT plans with 615 arcs have been used in the learning phase. The clinical gamma criteria used was 3% local dose/ 3mm/ treshold 20%. For the test phase, 92 plans with 146 arcs have been used. Results ROC Analysis has been performed on the learning database (Area Under The Curve = 0,85, Zero-one = 0,89), that is linked with the specific equipment used ( Elekta Synergy, Beam Modulator, Delta4, Pinnacle3, and different tumor sites (pelvis, prostate, H&N, thorax, brain, …)), and the specific acceptance criteria used. (3% local dose/ 3mm/ treshold 20%).

(Figure 1)

This model has been used for the test patient. Figure 2 shows the results of good prediction and time sparing for a given confidence level, that can be adjusted. The figures are calculated with a False OK rate >5% and a False KO rate > 15% and 95% of the probability distribution fulfilling the 2 criteria. If not, the advise would be to do the QA.

(Figure 2)

Conclusion We have shown that with this deep learning method, we could reduce the number of pre treatment patient QA by 45% (True OK/KO) and have a good efficiency of the global prediction of 89% (True OK/KO/TEST). The 11% left being good plans that will be tested. Further investigations in other radiation therapy centers are ongoing, and the Gamma values will also being studied, in order to give additional analysis tools to reduce the time spent for pre treatment patient QA. PO-1461 Benchmarking proton therapy water equivalent path length calculations against TPS algorithms K. Busch 1 , A.G. Andersen 1 , J.B.B. Petersen 1 , P.S. Skyt 1 , O. Nørrevang 2 , L.P. Muren 1 1 Aarhus University Hospital, Department of Oncology, Aarhus, Denmark ; 2 Aarhus University Hospital, Danish Centre of Particle Therapy, Aarhus, Denmark Purpose or Objective Proton therapy is sensitive towards inter-fractional organ motion and density variations due to the finite range of protons, potentially causing dose degradations. Range variations can be estimated by calculating the water equivalent path length (WEPL), which can be done quicker than full-fledge dose calculations using treatment planning system (TPS) algorithms. WEPL calculations have the potential to become a useful tool for applications involving online calculations with respect to plan robustness towards organ motion and density changes. Our aim was to compare WEPL calculations with dose re-calculation using a TPS algorithm. Material and Methods The WEPL-based isodose levels were calculated using two image modalities e.g. a planning CT (pCT) and a repeat CT (rCT), which were translated to stopping power values. A