SU-F-J-218: Predicting Radiation-Induced Xerostomia by Dosimetrically Accounting for Daily Setup Uncertainty During Head and Neck IMRT

• Published in: Medical physics (Lancaster) 2016-06, Vol.43 (6), p.3459-3459
• Main Authors: Park, S, Quon, H, McNutt, T, Plishker, W, Shekhar, R, Lee, J
• Format: Article
• Language: English
• Subjects: 60 APPLIED LIFE SCIENCES; ACCURACY; ALGORITHMS; COMPUTERIZED TOMOGRAPHY; Cone beam computed tomography; CORRECTIONS; DOSIMETRY; GLANDS; HEAD; Intensity modulated radiation therapy; ITERATIVE METHODS; NECK; PARTICLE TRACKS; PATIENTS; PLANNING; RADIATION DOSES; RADIATION PROTECTION AND DOSIMETRY; RADIOTHERAPY; RANDOMNESS; VALIDATION
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4956126

Purpose: To determine if the accumulated parotid dosimetry using planning CT to daily CBCT deformation and dose re-calculation can predict for radiation-induced xerostomia. Methods: To track and dosimetrically account for the effects of anatomical changes on the parotid glands, we propagated physicians’ contours from planning CT to daily CBCT using a deformable registration with iterative CBCT intensity correction. A surface mesh for each OAR was created with the deformation applied to the mesh to obtain the deformed parotid volumes. Daily dose was computed on the deformed CT and accumulated to the last fraction. For both the accumulated and the planned parotid dosimetry, we tested the prediction power of different dosimetric parameters including D90, D50, D10, mean, standard deviation, min/max dose to the combined parotids and patient age to severe xerostomia (NCI-CTCAE grade≥2 at 6 mo follow-up). We also tested the dosimetry to parotid sub-volumes. Three classification algorithms, random tree, support vector machine, and logistic regression were tested to predict severe xerostomia using a leave-one-out validation approach. Results: We tested our prediction model on 35 HN IMRT cases. Parameters from the accumulated dosimetry model demonstrated an 89% accuracy for predicting severe xerostomia. Compared to the planning dosimetry, the accumulated dose consistently demonstrated higher prediction power with all three classification algorithms, including 11%, 5% and 30% higher accuracy, sensitivity and specificity, respectively. Geometric division of the combined parotid glands into superior-inferior regions demonstrated ∼5% increased accuracy than the whole volume. The most influential ranked features include age, mean accumulated dose of the submandibular glands and the accumulated D90 of the superior parotid glands. Conclusion: We demonstrated that the accumulated parotid dosimetry using CT-CBCT registration and dose re-calculation more accurately predicts for severe xerostomia and that the superior portion of the parotid glands may be particularly important in predicting for severe xerostomia. This work was supported in part by NIH/NCI under grant R42CA137886 and in part by Toshiba big data research project funds.