¹⁸F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia.

Abstract

Current prediction of radiation-induced xerostomia 12months after radiotherapy (Xer_12m) is based on mean parotid gland dose and baseline xerostomia (Xer_baseline) scores. The hypothesis of this study was that prediction of Xer_12m is improved with patient-specific characteristics extracted from ¹⁸F-FDG PET images, quantified in PET image biomarkers (PET-IBMs). Intensity and textural PET-IBMs of the parotid gland were collected from pre-treatment ¹⁸F-FDG PET images of 161 head and neck cancer patients. Patient-rated toxicity was prospectively collected. Multivariable logistic regression models resulting from step-wise forward selection and Lasso regularisation were internally validated by bootstrapping. The reference model with parotid gland dose and Xer_baseline was compared with the resulting PET-IBM models. High values of the intensity PET-IBM (90th percentile (P90)) and textural PET-IBM (Long Run High Grey-level Emphasis 3 (LRHG3E)) were significantly associated with lower risk of Xer_12m. Both PET-IBMs significantly added in the prediction of Xer_12m to the reference model. The AUC increased from 0.73 (0.65-0.81) (reference model) to 0.77 (0.70-0.84) (P90) and 0.77 (0.69-0.84) (LRHG3E). Prediction of Xer_12m was significantly improved with pre-treatment PET-IBMs, indicating that high metabolic parotid gland activity is associated with lower risk of developing late xerostomia. This study highlights the potential of incorporating patient-specific PET-derived functional characteristics into NTCP model development.