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Adaptive radiotherapy is a growing field of study in cancer treatment due toit's objective in sparing healthy tissue. The standard of care in severalinstitutions includes longitudinal cone-beam computed tomography (CBCT)acquisitions to monitor changes, but have yet to be used to improve tumorcontrol while managing side-effects. The aim of this study is to demonstratethe clinical value of pre-treatment CBCT acquired daily during radiationtherapy treatment for head and neck cancers for the downstream task ofpredicting severe toxicity occurrence: reactive feeding tube (NG),hospitalization and radionecrosis. For this, we propose a deformable 3Dclassification pipeline that includes a component analyzing the Jacobian matrixof the deformation between planning CT and longitudinal CBCT, as well asclinical data. The model is based on a multi-branch 3D residual convolutionalneural network, while the CT to CBCT registration is based on a pair ofVoxelMorph architectures. Accuracies of 85.8% and 75.3% was found forradionecrosis and hospitalization, respectively, with similar performance asearly as after the first week of treatment. For NG tube risk, performanceimproves with increasing the timing of the CBCT fraction, reaching 83.1% afterthe $5_{th}$ week of treatment.

Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction