First Clinical Investigation of Cone Beam Computed Tomography and Deformable Registration for Adaptive Proton Therapy for Lung Cancer

Abstract

An adaptive proton therapy workflow using cone beam computed tomography (CBCT) is proposed. It consists of an online evaluation of a fast range-corrected dose distribution based on a virtual CT (vCT) scan. This can be followed by more accurate offline dose recalculation on the vCT scan, which can trigger a rescan CT (rCT) for replanning. The workflow was tested retrospectively for 20 consecutive lung cancer patients. A diffeomorphic Morphon algorithm was used to generate the lung vCT by deforming the average planning CT onto the CBCT scan. An additional correction step was applied to account for anatomic modifications that cannot be modeled by deformation alone. A set of clinical indicators for replanning were generated according to the water equivalent thickness (WET) and dose statistics and compared with those obtained on the rCT scan. The fast dose approximation consisted of warping the initial planned dose onto the vCT scan according to the changes in WET. The potential under- and over-ranges were assessed as a variation in WET at the target's distal surface. The range-corrected dose from the vCT scan reproduced clinical indicators similar to those of the rCT scan. The workflow performed well under different clinical scenarios, including atelectasis, lung reinflation, and different types of tumor response. Between the vCT and rCT scans, we found a difference in the measured 95% percentile of the over-range distribution of 3.4 ± 2.7 mm. The limitations of the technique consisted of inherent uncertainties in deformable registration and the drawbacks of CBCT imaging. The correction step was adequate when gross errors occurred but could not recover subtle anatomic or density changes in tumors with complex topology. A proton therapy workflow based on CBCT provided clinical indicators similar to those using rCT for patients with lung cancer with considerable anatomic changes.