Prediction of GTV median dose differences eases Monte Carlo re-prescription in lung SBRT

Abstract

Background and PurposeThe use of Monte Carlo (MC) dose calculation algorithm for lung patients treated with stereotactic body radiotherapy (SBRT) can be challenging. Prescription in low density media and time-consuming optimization conducted CyberKnife centers to propose an equivalent path length (EPL)-to-MC re-prescription method based on GTV median dose. Unknown at the time of planning, GTV D50% practical application remains difficult. The current study aims at creating a re-prescription predictive model in order to limit conflicting dose value during EPL optimization. Material and Methods129 patients planned with EPL algorithm were recalculated with MC. Relative GTV_D50% discrepancies were assessed and influencing parameters identified using wrapper feature selection. Based on best descriptive parameters, predictive nomogram was built from multivariate linear regression. EPL-to-MC OARs near max-dose discrepancies were reported. ResultsThe differences in GTV_D50% (median 10%, SD: 9%) between MC and EPL were significantly (p < .001) impacted by the lesion’s surface-to-volume ratio and the average relative electronic density of the GTV and the GTV’s 15 mm shell. Built upon those parameters, a nomogram (R2 = 0.79, SE = 4%) predicting the GTV_D50% discrepancies was created. Furthermore EPL-to-MC OAR dose tolerance limit showed a strong linear correlation with coefficient range [0.84–0.99]. ConclusionGood prediction on the required re-prescription can be achieved prior planning using our nomogram. Based on strong linear correlation between EPL and MC for OARs near max-dose, further restriction on dose constraints during the EPL optimization can be warranted. This a priori knowledge eases the re-prescription process in limiting conflicting dose value.