Classification of the source of treatment deviation in proton therapy using prompt-gamma imaging information

Purpose: Prompt-gamma imaging (PGI) based range verification has been successfully implemented in clinical proton therapy recently and its sensitivity to detect treatment deviations is currently investigated. The cause of treatment deviations can be multiple - e.g. CT-based range prediction, patient setup, and anatomical changes. Hence, it would be beneficial, if PGI-based verification would not only detect a treatment deviation but would also be able to directly identify its most probable source. Here, we propose a heuristically derived decision tree approach to automatically classify the sources of range deviation in proton pencil-beam scanning (PBS) treatments of head and neck tumors based on range information obtained with a PGI slit camera. Materials and Methods: The decision tree model was iteratively generated on a training dataset of different anatomical complexities, for an anthropomorphic head phantom and patient CT data (planning and control CTs) from 5 patients. Different range prediction errors, setup changes and relevant and non-relevant anatomical changes were introduced or derived from control CTs, summing up to a total of 98 training scenarios. Independent validation was performed for another 98 scenarios, derived solely from patient CT data of another 7 patients. PBS head and neck treatment plans were generated for the nominal scenario. For all PBS spots in the investigated field, PGI profiles were simulated using a dedicated analytical model of the slit camera for the nominal as well as the different error scenarios. From comparison of PGI profiles for nominal and error scenarios, a spot-wise range shift was determined for each error scenario. The heuristic approach includes a pre-filtering of the most suitable PBS spots for PGI treatment verification. From the validation, the accuracy, sensitivity and specificity of the model were determined. Results: A five-step consecutive filter was developed to pre-select PBS spots. On average, 31% of spots (