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

Abstract

Background and purposeCurrent prediction of radiation-induced xerostomia 12months after radiotherapy (Xer12m) is based on mean parotid gland dose and baseline xerostomia (Xerbaseline) scores. The hypothesis of this study was that prediction of Xer12m is improved with patient-specific characteristics extracted from 18F-FDG PET images, quantified in PET image biomarkers (PET-IBMs). Patients and methodsIntensity 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 Xerbaseline was compared with the resulting PET-IBM models. ResultsHigh 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 Xer12m. Both PET-IBMs significantly added in the prediction of Xer12m 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). ConclusionPrediction of Xer12m 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.