The Shape of Parotid DVH Predicts the Entity of Gland Deformation During IMRT for Head and Neck Cancers.

The Jacobian of the deformation field of the registration between images taken during Radiotherapy is a measure of compression/expansion of the voxels within an organ. The Jacobian mean value was applied to investigate possible correlations between parotid deformation and anatomical, clinical and dosimetric parameters. Data of 84 patients were analyzed. Parotid deformation was evaluated through Jacobian maps of images taken at the start and at the end of the treatment. Several clinical, geometrical and dosimetric factors were considered. Correlation between Jacobian mean value and these parameters was assessed through Spearman's test. Univariate and multivariate logistic analyses were performed by considering as the end point the first quartile value of the Jacobian mean value. Parotid dose volume histograms were stratified according to gland deformation, assessing the most predictive dose-volume combination. At multivariate analysis, age (p = 0.02), overlap between tumor volume and parotid gland (p = 0.0006) and the parotid volume receiving more than 10 Gy (p = 0.02) were found as the best independent predictors, by considering Jacobian mean value <first quartile as the end point. By comparing the average dose volume histogram of parotids with Jacobian mean value fist quartile, the parotid volume receiving more than 10 Gy and 40 Gy were found as the most predictive dosimetric parameters. Parotid glands were divided in three different sub-groups (bad-, medium- and good dose volume histogram). The risk to have Jacobian means value lower than first quartile was 39.6% versus 19.6% versus 11.3% in these three groups. By including in the multivariate analysis this "dose volume grouping" parameter, age and bad dose volume histogram were found as the most predictive parameters for large shrinkage. The pattern of parotid deformation may be well predicted by some pre-treatment variables; a bad dose volume histogram seems the most important predictor.