ESTRO 2023 - Abstract Book

S1335

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ESTRO 2023

Fig. 2 compares the Full, A and B models. The only statistically significant differences between A and B plans were the constrictors, left parotid and left submandibular, with a maximum difference of 1.11Gy.

Conclusion The four KBP models created largely equivalent plans with only a handful of significant differences of 1.11Gy or less. We did not see a substantial benefit from doubling the training of the KBP model, however, the “small” training sets of 101 plans represent a relatively large training set. Furthermore, planning preferences by planners or physicians in a KBP training set only lead to minimal differences in resulting plans. These findings indicate that it is prudent to train a general KBP model for a disease site rather than creating a multitude of models with physician specific planning preferences.

PO-1639 Autosegmentation of structures for Cranial Spinal Irradiation patients using Deep Learning

J. Kallehauge 1 , R. Worawongsakul 2 , O. Nørrevang 1 , K. Seiersen 1 , Y. Lassen-Ramshad 1

1 Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; 2 Ramathibodi Hospital, Mahidol University, Ratchathewi, Department of Diagnostic and Therapeutic Radiology, Bangkok, Thailand Purpose or Objective Craniospinal irradiation (CSI) is indicated for embryonal brain tumours and leptomeningeal metastasis. Contouring of target and normal tissue structures for CSI is cumbersome and requires high level expertise to ensure favorable outcomes. The delineation process may easily take more than a day's work even for an experienced physician, however Deep Learning has the potential to significantly reduce the time spent on this process and at the same time improve consistency. The aim of this study was to evaluate the performance of a convolutional neural network (CNN) for segmentation of target and normal tissue organs for CSI. Materials and Methods Twenty-two individual patient scans were delineated according to the International Pediatric Oncology Society (SIOP) guidelines and used to train and validated four consecutive CNNs while twelve individual patient scans were used for independent testing. Thirty-three individual structures were used for training of which thirteen were related to target and the remaining to normal tissue structures. The performance was evaluated using Dice similarity score (DSC) and the 95th percentile Haussdorff distance (HD95). (Figure 1)