ESTRO 2023 - Abstract Book

S551

Sunday 14 May 2023

ESTRO 2023

Conclusion The performance of the nnU-NET auto-segmentation model trained only on adult images was satisfactory for application to paediatric CT images (mean DSC > 0.71), however, the inclusion of paediatric images in the training data significantly improved the accuracy of paediatric contouring. The combined-training model performed as well as a paediatric-specific model, indicating that separate adult and paediatric models are not required. PD-0660 CBCT Deep Learning Model to Predict Extreme Prostate Motion Verified by Ultrasound Probe C.G.A. Chua 1 , E.P.P. Pang 1 , W.Y.C. Koh 1 , A.D. Abdul Mutalib 1 , W.C. Chong 1 , K.S. Lew 1 , P.L. Yeap 1 , K.W. Ang 1 , S.Y. Park 1,2 , J.C.L. Lee 1 , J.K.L. Tuan 1,2 , H.Q. Tan 1 1 National Cancer Centre Singapore, Division of Radiation Oncology, Singapore, Singapore; 2 Duke NUS Medical School, Oncology Academic Clinical Program, Singapore, Singapore Purpose or Objective It is known that organs in pelvis including prostate are moving in milliseconds timeframe under the influence of rectal volume, bladder volume, and change of muscle tension. These intrafractional motions are known to be stochastic and could degrade the dose distributions. As such, this study aimed to investigate the hypothesis if deep learning applied on CBCT image acquired prior to actual daily treatment could predict extreme prostate motion during the treatment course. Materials and Methods 24 patients who had CBCT scans with intrafraction motion monitoring in place using Clarity (Elekta AB, Stockholm, Sweden), a 4D transperineal ultrasound device. A total of 465 pretreatment CBCT images and 58.9 hours of intrafraction prostate displacement data were used to train and test a ‘built from scratch’ 3D Convolutional Neural Network (CNN) to predict intrafractional shift in prostate. Each pretreatment CBCT images has a corresponding ultrasound time trace signal obtained during treatment. The CBCT images were binarily classified based on the corresponding intrafraction motion ultrasound time trace during treatment. When the vector length exceeds a threshold of 5 mm for 5 seconds, the CBCT image is classified as an extreme prostate motion event. The images were stratified by treatment fractions and the model was trained and tested on every image through a 5-fold cross validation. The architecture of the 3D CNN used is adapted from Hasib et al (2020). The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used for the model evaluations and Brier Score was calculated to measure the accuracy of probabilistic predictions. Gradient-weighted Class Activation Mapping (Grad-CAM) of the model was generated to provide insights of model's learning activity. Results The mean AUC score of 0.82 ± 0.02 (mean ± std) and Brier Score of 0.182 on validation dataset and AUC score 0.78 ± 0.03 and Brier Score of 0.201 on test dataset with the Brier Score were shown in Figure 1. Grad-CAM images from 2 true negative and 2 true positive case in Figure 2 suggest that the bladder, femur and rectum could be a factor in predicting prostate motion. An interesting note is the prostate does not seem contribute as much in the prediction.