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Positron Emission Tomography (PET)-Guided Dynamic Lung Tumor Tracking for Cancer Radiotherapy: First Patient Simulations

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J Yang

J Yang1*, T Yamamoto2 , B Loo1 , E Graves1 , P Keall3 , (1) Stanford University, Stanford, CA, (2) UC Davis School of Medicine, Sacramento, CA, (3) University of Sydney, Camperdown, Australia.


WE-G-BRF-6 Wednesday 4:30PM - 6:00PM Room: Ballroom F

Purpose: PET-guided dynamic tumor tracking is a novel concept of biologically targeted image guidance for radiotherapy. A dynamic tumor tracking algorithm based on list-mode PET data has been developed and previously tested on dynamic phantom data. In this study, we investigate if dynamic tumor tracking is clinically feasible by applying the method to lung cancer patient PET data.

Methods: PET-guided tumor tracking estimates the target position of a segmented volume in PET images reconstructed continuously from accumulated coincidence events correlated with external respiratory motion, simulating real-time applications, i.e., only data up to the current time point is used to estimate the target position. A target volume is segmented with a 50% threshold, consistently, of the maximum intensity in the predetermined volume of interest. Through this algorithm, the PET-estimated trajectories are quantified from four lung cancer patients who have distinct tumor location and size. The accuracy of the PET-estimated trajectories is evaluated by comparing to external respiratory motion because the ground-truth of tumor motion is not known in patients; however, previous phantom studies demonstrated sub-2mm accuracy using clinically derived 3D tumor motion.

Results: The overall similarity of motion patterns between the PET-estimated trajectories and the external respiratory traces implies that the PET-guided tracking algorithm can provide an acceptable level of targeting accuracy. However, there are variations in the tracking accuracy between tumors due to the quality of the segmentation which depends on target-to-background ratio, tumor location and size.

Conclusion: For the first time, a dynamic tumor tracking algorithm has been applied to lung cancer patient PET data, demonstrating clinical feasibility of real-time tumor tracking for integrated PET-linacs. The target-to-background ratio is a significant factor determining accuracy: screening during treatment planning would enable appropriate patient selection for PET-guided dynamic tumor tracking in radiotherapy.

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