ESTRO 2023 - Abstract Book

S1321

Digital Posters

ESTRO 2023

A total of 30 primary prostate cancer patients treated on the MR-Linac were included. For each patient, sCTs were generated from T2w-MRIs using a generative adversarial neural network (GAN). OARs were automatically contoured on the T2w-MRI and sCT using ART-Plan™ (TheraPanacea, Paris, France), respectively. MRIs were delineated by an AI-based MR segmentation method, which was trained on 235 T2w-MRIs. For the sCTs, a commercial, pretrained AI-based CT segmentation algorithm was used. The structures anal canal, bladder, femur left/right and rectum were considered for evaluation. Comparison was performed by calculating Hausdorff Distance (HD), 95% percentile HD (HD95), volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), path length (PL) and added path length (APL) using MATLAB (R2020a). Results Results were successfully generated for 28/30 data sets. Lowest median HD and HD95 were observed for anal canal (HD=7.2 mm) and bladder (HD95=2.0 mm). In terms of median vDSC, bladder showed the highest agreement with 0.97, while lowest agreement was observed for anal canal with 0.71. Median sDSC ranged from 0.48 for anal canal to 0.94 for rectum. The lowest median APL was determined for anal canal with 57.4 cm. Table 1 Comparison of the investigated structures for each calculated metric. Shown is the median [range] value of the respective metric.

Figure 1 Visualization of the calculated metrics for each evaluated structure.

Conclusion Detailed analysis of AI-based segmentation for two different AI-algorithms on different image modalities demonstrated in comparison acceptable deviations. While the auto-segmentation of bladder and rectum was reproduced correctly for most of the patients, anal canal showed lower agreement regarding vDSC and sDSC. Details may also be due to differences in the training contours of the used MRI and CT delineation algorithms. As the MRI provides superior soft tissue contrast for delineation of target volumes and OARs, the use of sCT for automatic segmentation of bony structures appears reasonable for an online MRgRT workflow.

PO-1626 Knowledge-based planning for meningioma

D. Kope ć 1 , A. Zawadzka 1 , D. Bodzak 1 , P. Kukolowicz 1 , K. Dyttus-Cebulok 2

1 Maria Sk ł odowska-Curie National Research Institute of Oncology in Warsaw, Medical Physics Department, Warsaw, Poland; 2 Maria Sk ł odowska-Curie National Research Institute of Oncology in Warsaw, Radiotherapy Department, Warsaw, Poland