ESTRO 2023 - Abstract Book

S534

Sunday 14 May 2023

ESTRO 2023

Conclusion The risk of heart morbidity in the decades following lymphoma treatment remains a challenge. Radiotherapy dose to the heart and anthracycline containing chemotherapy regimens show independent toxicity profiles specific to the endpoints CHF, IHD and VHD. Knowledge of the expected dose to the heart is essential in minimizing the risk of cardiotoxicity when evaluating alternative treatment approaches for lymphoma patients. MO-0642 Predicting post-radiotherapy cardiac toxicity for non-small cell lung cancer patients Z. Shen 1 , M. A. Hawkins 1,2 , D. Brand 1,2 , Y. Zhang 1 , O. Chohan 2,1 , J. Watts 1 , M. Simard 1 , C. Collins Fekete 1 1 University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom; 2 University College London Hospitals NHS Foundation Trust, Department of Radiotherapy, London, United Kingdom Purpose or Objective Cardiac toxicity after thoracic radiotherapy (RT) for lung cancer is a potentially lethal side effect. Long term outcomes from the RTOG-0617 dose-escalation trial show that higher dose to the heart is associated with shorter overall survival. However, models incorporating risk factors beyond dosimetry may better predict cardiac events. Here we apply artificial intelligence (AI) methods incorporating clinical, anatomical and dose data to model cardiac events in RTOG-0617. Materials and Methods (1) Data: The RTOG-0617 trial compared standard dose (60 Gy) vs high dose (74 Gy) radiotherapy for stage III non-small cell lung cancer (NSCLC) patients receiving chemo-RT (concurrent Paclitaxel ± Cetuximab). The data of 404 trial participants was used for model training (70%), validation (15%) and hold-out testing (15%). Patients with grade 3+ adverse events (graded by CTCAE version 3.0) related to heart were labelled as positive (37/404, 9.2%). (2) Methods: The planning CT scan, RT dose map and heart contour of the patients were combined as three-channel 3D volumes with a shape of (20 slices, 96 pixels, 96 pixels). A pretrained 3D ResNet18 processed the three-channel image input, and nine selected features were integrated by fully connected layers. The concatenated output was processed by multilayer perceptrons (MLPs) to predict the binary toxicity label. The architecture of the whole model is illustrated in Figure 1 .

The AI model was compared to a multivariate logistic regression model trained with clinical variables and dosimetric parameters listed in Table 1 .