Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis. J Appl Clin Med Phys. 2017 Jun 29;: Authors: Ono T, Nakamura M, Hirose Y, Kitsuda K, Ono Y, Ishigaki T, Hiraoka M Abstract To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within ...
Source: Journal of Applied Clinical Medical Physics - Category: Physics Authors: Tags: J Appl Clin Med Phys Source Type: research