ESTRO 2023 - Abstract Book

S499

Sunday 14 May 2023

ESTRO 2023

Figure 1: Clinical decision diagram representing the preferred modality for each patient. ∆ NTCP predicted is the difference between the NTCP values computed from the XT and PT plans. ∆ NTCP groundtruth is the difference between the NTCP values computed from the XT and PT manually generated plans. Conclusion In this work, we designed an automated clinical decision tool which consists in chaining DL dose prediction models with a NTCP model for pulmonary complications and we demonstrated the accuracy of this tool in predicting the preferred treatment modality for esophageal cancer patients.

OC-0614 A deep learning based dose engine M. Witte 1 , J. Sonke 1 1 The Netherlands Cancer Institute, Radiation oncology, Amsterdam, The Netherlands

Purpose or Objective Despite GPU acceleration, state-of-the-art Monte Carlo (MC) dose engines require considerable computation time to reduce stochastic noise. We developed a deep learning (DL) based dose engine, aiming to reach high accuracy at reduced computation times. Materials and Methods Following IRB approval, 350 planning CTs and RTPLANs were collected for a variety of tumor sites (step & shoot IMRT and VMAT). The GPUMCD standalone dose calculation library (Version 1, Elekta AB, Stockholm, Sweden) at a 1% (1SD) statistical uncertainty setting was used to compute 3D dose for 29100 separate segments at 6 and 10MV beam energies, both flattened and flattening filter free (FFF). A custom neural network was developed, combining convolutions and recurrences using 50 hidden layers, taking RTPLAN parameters and 3D CT as input. Parameters were trained minimizing MSELoss between the MC computed dose and the model output. The DL model was then used to reconstruct the full dose distribution for 6 plans not present in the training set. Gamma analyses (using local dose) were performed on the high dose region (above 50% of maximum dose) to assess accuracy. Model evaluations were performed on a PC with Nvidia RTX A4000 GPU. Results Trained dose distributions in general corresponded well to the MC results, discrepancies mainly arising in case of tissue inhomogeneity (see Fig. 1). Run times depended on PTV, but were less than 30 seconds for the DL dose engine in all cases, and between 10 to 100 times shorter than the corresponding MC computation (Table 1). Gamma pass rates at 2% tolerance were above 95% for all but the IMRT lung case, in which considerable volumes of lung tissue received more than 50% of maximum dose. At 1% tolerance the gamma pass rates were considerably lower.

Fig 1. Monte Carlo (green) and DL (purple) dose distributions overlaid for a dual arc VMAT rectum case (left) and a 7 beam step & shoot IMRT lung case (right). Dose profiles along the dotted lines are shown.

Table 1. Computation times and gamma evaluation results.