Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments.

PURPOSE

To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT).

METHODS

Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (r ) and the equivalent radius of volume that receives a set of prescription dose (r , where V  = V , V … V ). To evaluate the model's predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., ΔGM = GM - GM and fractional ΔV  = (V - V )/V ) and a coefficient of determination, R . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency.

RESULTS

Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV . The R for the linear fit between r and r was 0.985 ± 0.019 for isodose volumes ranging from V to V ; particularly, R  = 0.995 for V and R  = 0.997 for V . Compared to the training plans, our KBP test plan nCI was improved from 1.31 ± 0.15 to 1.15 ± 0.08 (P < 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner's intervention.

CONCLUSION

We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.