Sparing healthy tissue and increasing tumor dose using bayesian modeling of geometric uncertainties for planning target volume personalization

OBJECTIVE: To develop a mathematical tool that can update a patient's planning target volume (PTV) partway through a course of radiation therapy to more precisely target the tumor for the remainder of treatment and reduce dose to surrounding healthy tissue. METHODS AND MATERIALS: Daily on-board imaging was used to collect large datasets of displacements for patients undergoing external beam radiation therapy for solid tumors. Bayesian statistical modeling of these geometric uncertainties was used to optimally trade off between displacement data collected from previously treated patients and the progressively accumulating data from a patient currently partway through treatment, to optimally predict future displacements for that patient. These predictions were used to update the PTV position and margin width for the remainder of treatment, such that the clinical target volume (CTV) was more precisely targeted. RESULTS: Software simulation of dose to CTV and normal tissue for 2 real prostate displacement datasets consisting of 146 and 290 patients treated with a minimum of 30 fractions each showed that re-evaluating the PTV position and margin width after 8 treatment fractions reduced healthy tissue dose by 19% and 17%, respectively, while maintaining CTV dose. CONCLUSION: Incorporating patient-specific displacement patterns from early in a course of treatment allows PTV adaptation for the remainder of treatment. This substantially reduces the dose to healthy tissues and thus can reduce radiation therapy-induced toxicities, improving patient outcomes