ESTRO 2022 - Abstract Book

S1253

Abstract book

ESTRO 2022

Conclusion corrCBCT and vCT images provide accurate dose calculations, highly suitable to assist any strategy for offline dose-based adaptive radiotherapy, without needs for individual CBCT calibration or limitations to certain anatomical sites, CBCT acquisition protocols, or planning CT modality.

PO-1478 Convolutional recurrent neural networks for future anatomy prediction

D. Page 1 , A. McWilliam 1 , R. Chuter 2 , A. Green 1

1 University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom; 2 The Christie NHS Foundation Trust, Medical Physics and Engineering, Manchester, United Kingdom Purpose or Objective Highly conformal dose distributions allow for increased dose to target volumes without a significant increase in dose to nearby organs at risk (OARs). Such dose distributions can become unsafe to deliver due to changes in patient anatomy, such as tumour shrinkage or weight loss. This mandates the implementation of adaptive radiotherapy (ART), which can be resource intensive and time consuming. In this work, we investigate the feasibility of using a convolutional recurrent neural network (CRNN) to predict when replanning will be necessary, allowing for efficient, ahead-of-time resource allocation. Materials and Methods A time dependent CRNN was developed capable of predicting the future anatomy of head and neck (HN) cancer patients undergoing radiotherapy and the accuracy of predictions at different time points in the future was investigated. Series of 3D CBCTs obtained over a course of treatment, which varied between 5 and 31 in length, from 266 HN patients were used in this work, a total of 2849 images. For each series, the scans were rigidly registered on the target region. The