Principal Component Analysis-Based Anatomical Motion Models For Use In Adaptive Radiation Therapy Of Head And Neck Cancer Patients

摘要

Purpose: To develop standard and regularized principal component analysis (PCA) models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (Hu26N) patients, assess their potential use in adaptive radiation therapy (ART), and to extract quantitative information for treatment response assessment.Methods: Planning CT (pCT) images of Hu26N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during Radiation Therapy (RT). Artificial deformations closely mirrored patients’ actual deformations, and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms), and between pCT and clinical CBCTs. Patient-specific standard PCA (SPCA) and regularized PCA (RPCA) models were built from these synthetic and clinical DVF sets. Eigenvectors, or eigenDVFs (EDVFs), having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Modeled anatomies were used to assess the dose deviations with respect to the planned dose distribution.Results: PCA models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade SPCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes, and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. For dose assessment it has been shown that the modeled dose distribution was different from the planned dose for the parotid glands due to their shrinkage and shift into the higher dose volumes during the radiotherapy course. Modeled DVHs still underestimated the effect of parotid shrinkage due to the large compression factor (CF) used to acquire DVFs.Conclusion: Leading EDVFs from both PCA approaches have the potential to capture systematic anatomical changes during Hu26N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable than SPCA at capturing systematic changes, enabling dosimetric consequences to be projected to the future treatment fractions based on trends established early in a treatment course, or, potentially, based on population models. This work showed that PCA has a potential in identifying the major mode of anatomical changes during the radiotherapy course and subsequent use of this information in future dose predictions is feasible. Use of smaller CF values for DVFs is preferred, otherwise anatomical motion will be underestimated.