ESTRO 2023 - Abstract Book

S1900

Digital Posters

ESTRO 2023

input (D2). With this CNN, three outcome prediction models using the following different groups of image input were evaluated: CT and PET (M5); CT, PET and GTVp (M6); CT, PET, GTVp and GTVn (M7). Results For DFS (Figure 1), models based on clinical input data (M1-M4) had the overall poorest external validation results due to their considerably low MCC and class 0 F1 scores, reflecting a high number of false positive predictions for the MAASTRO dataset. The CNNs trained on CT and PET images, with and without the GTVp (M5&M6), obtained the highest performance metrics. For OS (Figure 2), only CNN models (M5-M7) maintained their performance under external validation while other models (M1-M4) overfitted to the OUS dataset as indicated by the decrease in performance for the MAASTRO set. Model M6, trained on CT, PET and GTVp, obtained the best performance in almost all metrics. Conclusion CNN models based on CT and PET images without inclusion of clinical data can achieve better and more generalized performance in a multi-center setting when predicting DFS and OS than models based solely on clinical information.