Adaptive radiotherapy (ART) is commonly used to mitigate effects of anatomical change during head and neck (H&N) radiotherapy. The process of identifying patients for ART can be subjective and resource-intensive. This feasibility project aims to design and validate a pipeline to automate the process and use it to assess the current clinical pathway for H&N treatments.

The pipeline analysed patients’ on-set cone-beam CT (CBCT) scans to identify inter-fractional anatomical changes. CBCTs were converted into synthetic CTs, contours were automatically generated, and the original plan was recomputed. Each synthetic CT was evaluated against a set of dosimetric goals, with failed goals causing an ART recommendation.

To validate pipeline performance, a ‘gold standard’ was synthesised by recomputing patients’ original plans on a rescan-CT acquired during treatment and identifying failed clinical goals. The pipeline sensitivity and specificity compared to this ‘gold standard’ were calculated for 12 ART patients. The pipeline was then run on a cohort of 12 ART and 14 non-ART patients, and its sensitivity and specificity were instead calculated against the clinical decision made.

The pipeline showed good agreement with the synthesised ‘gold standard’ with an optimum sensitivity of 0·83 and specificity of 0·67. When run over a cohort containing both ART and non-ART patients and assessed against the subjective clinical decision made, the pipeline showed no predictive power (sensitivity: 0·58, specificity: 0·47).

Good agreement with the ‘gold standard’ gives confidence in pipeline performance and disagreement with clinical decisions implies implementation could help standardise the current clinical pathway.