Targeting accuracy in real-time tumor tracking via external surrogates: a comparative study.

The use of external surrogates to predict tumor motion in real-time for extra-cranial sites requires the use of accurate correlation models. This is extremely challenging when motion prediction is to be performed over several breathing cycles, as occurs for real-time tumor tracking with Cyberknife((R)) Synchrony((R)). In this work we compare three different approaches to infer tumor motion based on external surrogates, since no comparative study is available to assess the accuracy of correlation models in tumor tracking over a long time period. We selected 20 cases in a database of 130 patients treated with real-time tumor tracking by means of the Synchrony((R)) module. The implemented correlation models comprise linear/quadratic correlation, artificial neural networks and fuzzy logic. The accuracy of each correlation model is evaluated on the basis of ground truth tumor position information acquired during treatment, as detected by means of stereoscopic X-ray imaging. Results show that the implemented models achieve an error reduction with respect to Synchrony((R)), measured at the 95% confidence level, up to 10.8% for the fuzzy logic approach. This latter is able to partly reduce the incidence of tumor tracking errors above 6 mm, resulting in improved accuracy for larger discrepancies. In conclusion, complex models are suggested to predict tumor motion over long time periods. This leads to an effective improvement with respect to Cyberknife((R)) Synchrony((R)). Future studies will investigate the sensitivity of the implemented models to the input database, in order to define optimal strategies.