A model-based patient selection tool to identify who may be at risk of exceeding dose tolerances during pancreatic SBRT. (December 2019) - Magallon-Baro, Alba; Granton, Patrick V.; Milder, Maaike T.W.; Loi, Mauro; Zolnay, Andras G.; Nuyttens, Joost J.; Hoogeman, Mischa S.
Highlights: Daily anatomical variations led to extra dose to OAR for around 60% of our patients. An OAR motion model can be used to evaluate dosimetric uncertainties on new patients. Sensitivity of planned doses to organ motion can be predicted on planning anatomy. Pancreatic patients can be stratified according to individual organ risk simulations. Model-based predictions outperform geometric measures predictions on pCT anatomy. Abstract: Purpose: Locally advanced pancreatic cancer (LAPC) patients are prone to experience daily anatomical variations, which can lead to additional doses in organs-at-risk (OAR) during SBRT. A patient selection tool was developed to identify who may be at risk of exceeding dose tolerances, by quantifying the dosimetric impact of daily variations using an OAR motion model. Materials and methods: The study included 133 CT scans from 35 LAPC patients. By following a leave-one-out approach, an OAR motion model trained with the remaining 34 subjects variations was used to simulate organ deformations on the left-out patient planning CT anatomy. Dose–volume histograms obtained from planned doses sampled on simulated organs resulted in the probability of exceeding OAR dose-constraints due to anatomical variations. Simulated probabilities were clustered with a threshold per organ according to clinical observations. If the prediction of at least one OAR was above the established thresholds, the patient was classified as being at risk. Results: Clinically, in 20/35 patients at least one OAR exceeded dose-constraints in the daily CTs. The model-based prediction had an accuracy of 89%, 71%, 91% in estimating the risk of exceeding dose tolerances for the duodenum, stomach and bowel, respectively. By combining the three predictions, our approach resulted in a correct patient classification for 29/35 patients (83%) when compared with clinical observations. Conclusions: Our model-based patient selection tool is able to predict who might be at risk of exceeding dose-constraints during SBRT. It is a promising tool to tailor LAPC treatments, e.g. by employing online adaptive SBRT; and hence, to minimize toxicity of patients being at risk.