Target volume and artifact evaluation of a new data-driven 4D CT

Abstract

PurposeFour-dimensional computed tomography (4D CT) is often used to define the internal gross target volume (IGTV) for radiation therapy of lung cancer. Traditionally, this technique requires the use of an external motion surrogate; however, a new image, data-driven 4D CT, has become available. This study aims to describe this data-driven 4D CT and compare target contours created with it to those created using standard 4D CT. Methods and materialsCine CT data of 35 patients undergoing stereotactic body radiation therapy were collected and sorted into phases using standard and data-driven 4D CT. IGTV contours were drawn using a semiautomated method on maximum intensity projection images of both 4D CT methods. Errors resulting from reproducibility of the method were characterized. A comparison of phase image artifacts was made using a normalized cross-correlation method that assigned a score from +1 (data-driven “better”) to –1 (standard “better”). ResultsThe volume difference between the data-driven and standard IGTVs was not significant (data driven was 2.1 ± 1.0% smaller, P = .08). The Dice similarity coefficient showed good similarity between the contours (0.949 ± 0.006). The mean surface separation was 0.4 ± 0.1 mm and the Hausdorff distance was 3.1 ± 0.4 mm. An average artifact score of +0.37 indicated that the data-driven method had significantly fewer and/or less severe artifacts than the standard method (P = 1.5 × 10-5 for difference from 0). ConclusionsOn average, the difference between IGTVs derived from data-driven and standard 4D CT was not clinically relevant or statistically significant, suggesting data-driven 4D CT can be used in place of standard 4D CT without adjustments to IGTVs. The relatively large differences in some patients were usually attributed to limitations in automatic contouring or differences in artifacts. Artifact reduction and setup simplicity suggest a clinical advantage to data-driven 4D CT.