Multicenter approach to predict plan quality of robotic intracranial SRS/SRT

Abstract

Purpose

This study analyzed inter-institute conformity and dose gradient variability of CyberKnife (CK) brain SRS/SRT plans. The feasibility of multi-center predictive models was investigated, aiming at guided/automated planning optimization.

Methods

Data from 335 clinical plans, delivered for single lesions in 1–5 fractions, were collected by 8 CK centers. Conformity index (CI), Dose Gradient Index (DGI) and the effective radii defined by different isodose volumes (Reff) were computed. Predictability of dose fall-off from PTV dimensions was analyzed. DGI average, 80th and 10^thpercentile values were evaluated stratifying plans by PTV size into six groups. Linear regression models were created for Reff as a function of PTV equivalent radius.

Results

CI values (range 0.96–––2.23) exceeded 1.20 in 88/335 plans, mostly (65 %) collected in 2 of the participating centers. DGI showed an acceptable inter-institute variability and a strong significant correlation (p < 0.0001) with PTV. Ideal and Minimal DGI for each of the six groups were respectively 95 (86), 82 (73), 77 (68), 71 (60), 59 (43) and 50 (29). The rate of DGI values passing the multicenter minimal criteria, considering each center separately, varied from 43 % to 100 %. R² values for the regression between Reff and PTV radius were ≥ 0.958, showing an increasing inter-center variability for decreasing isodose values.

Conclusion

Observed inter-center differences enhanced the advantages of a multi-institute approach. Multicenter predictive models for dose fall-off in CK brain SR/SRT planning are feasible and easy to use. Reff models and DGI analysis may permit to partially automate planning optimization avoiding creation of suboptimal plans.