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.

The results demonstrate that high sensitivity in toxicity prediction can be achieved with optimized ANNs, that are put forward to represent a valuable support in medical decisions. Future studies will be focused on enlarging the available patient database to increase the reliability of toxicity prediction algorithms and to define optimal classification criteria.

In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy‐based and one neuro‐fuzzy–based inference systems with respect to state‐of‐the‐art models. Finally, due to the intrinsic interpatient variability in fuzzy models' performance, a model selectivity algorithm was proposed to select an adaptive fuzzy modeler on a case‐by‐case basis. The performance of multiple and adaptive fuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real‐time tumor tracking. Final results show that activating adequate model selection of our fuzzy‐based modeler can significantly reduce tumor tracking errors.PACS number: 87

In image guided radiotherapy, in order to reach a prescribed uniform dose in dynamic tumors at thorax region while minimizing the amount of additional dose received by the surrounding healthy tissues, tumor motion must be tracked in real-time. Several correlation models have been proposed in recent years to provide tumor position information as a function of time in radiotherapy with external surrogates. However, developing an accurate correlation model is still a challenge. In this study, we proposed an adaptive neuro-fuzzy based correlation model that employs several data clustering algorithms for antecedent parameters construction to avoid over-fitting and to achieve an appropriate performance in tumor motion tracking compared with the conventional models. To begin, a comparative assessment is done between seven nuero-fuzzy correlation models each constructed using a unique data clustering algorithm. Then, each of the constructed models are combined within an adaptive sevenfold synthetic model since our tumor motion database has high degrees of variability and that each model has its intrinsic properties at motion tracking. In the proposed sevenfold synthetic model, best model is selected adaptively at pre-treatment. The model also updates the steps for each patient using an automatic model selectivity subroutine. We tested the efficacy of the proposed synthetic model on twenty patients (divided equally into two control and worst groups) treated with CyberKnife synchrony system. Compared to Cyberknife model, the proposed synthetic model resulted in 61.2% and 49.3% reduction in tumor tracking error in worst and control group, respectively. These results suggest that the proposed model selection program in our synthetic neuro-fuzzy model can significantly reduce tumor tracking errors. Numerical assessments confirmed that the proposed synthetic model is able to track tumor motion in real time with high accuracy during treatment.

At external beam radiotherapy for some tumors located at thorax region due to lack of information in gray scale fluoroscopic images tumor position determination is problematic. One of the clinical strategies is to implant clip as internal fiducial marker inside or near tumor to represent tumor position while the contrast of implanted clip is highly observable rather than tumor. As alternative, using natural anatomical landmarks located at thorax region of patient body is proposed to extract tumor position information without implanting clips that is invasive method with possible side effect. Among natural landmarks, ribs of rib-cage structure that result proper visualization at X-ray images may be optimal as representative for tumor motion. In this study, we investigated the existence of possible correlation between ribs as natural anatomical landmarks and various lung and liver tumors located at different sites as challenging issue. A simulation study was performed using data extracted from 4-dimensional extended cardiac-torso anthropomorphic phantom that is able to simulate motion effect of dynamic organs, as well. Several tumor sites with predefined distances originated from chosen ribs at anterior-posterior direction were simulated at 3 upper, middle, and lower parts of chest. Correlation coefficient between ribs and tumors was calculated to investigate the robustness of ribs as anatomical landmarks for tumor motion tracking. Moreover, a consistent correlation model was taken into account to track tumor motion with a rib as best candidate among selected ribs. Final results represent availability of using rib cage as anatomical landmark to track lung and liver tumors in a noninvasive way. Observations of our calculations showed a proper correlation between tumors and ribs while the degree of this correlation is changing depends on tumor site while lung tumors are more varied and complex with less correlation with ribs motion against liver tumors.