Mitigating differential baseline shifts in locally advanced lung cancer patients using an average anatomy model

Differential baseline shifts between primary tumor and involved lymph nodes in locally advanced lung cancer patients compromise the accuracy of radiotherapy. The purpose of this study was to evaluate the performance of an average anatomy model (AAM) derived from repeat imaging and deformable registration to reduce these geometrical uncertainties.

An in-house implementation of a B-Spline deformable image registration (DIR) algorithm was first validated using three different validation approaches: (a) a circle method to test the consistency of the DIR, (b) fiducial marker target registration error, and (c) the recovery of a known deformation vector field (DVF). Subsequently, AAM was generated by first averaging five DVFs resulting from cone beam CT (CBCT) to planning CT (pCT) DIR and second by applying the inverse of the average DVF to the pCT. The proposed method was evaluated on 15 locally advanced lung cancer patients receiving daily motion compensated CBCT and a repeat CT (rCT) for adaptive radiotherapy. Reduction of systematic baseline shifts of the primary tumor were quantified for the fractions used to build the AAM as well as over the whole treatment and compared to the performance of the rCT.

The deformable registration accuracy was ≤ 2 mm vector length for the first two validation methods and about 3 mm for the third method. The systematic baseline shifts over the five fractions prior to the rCT used to build the AAM reduced from 5.9 mm vector length relative to the pCT to 2.3 and 4.2 mm relative to the AAM and rCT, respectively. The overall systematic errors in the left-right, cranio-caudal, and anterior-posterior directions were [3.4, 3.8, 3.3] mm, [2.3, 2.9, 2.6] mm, and [2.3, 3.1, 2.7] mm for the pCT, AAM, and rCT, respectively.

The AAM mitigates systematic errors occurring during treatment due to differential baseline shifts between the primary tumor and involved lymph nodes similar to (or even better than) rCT. The superior performance of the AAM in terms of the systematic error derived from the initial fractions indicates that further analysis of the optimum intervention time is required. This model has the potential to be used as an efficient and accurate alternative for rCT in adaptive radiotherapy of locally advanced lung cancer patients, obviating the need for rescanning and recontouring.