PSMA PET/CT as a predictive tool for sub-regional importance estimates in the parotid gland

TL;DR

This study explores the use of PSMA PET/CT imaging combined with radiomic analysis to predict subregional importance and dose sensitivity in parotid glands, aiming to improve management of radiation-induced xerostomia.

Contribution

It introduces a novel predictive model using PSMA PET radiomic features to estimate subregional importance in parotid glands, incorporating patient-specific deviations.

Findings

Anticorrelations between PSMA PET uptake and importance models.

Kernel Ridge Regression with PCA achieved MAE of 0.08 on test data.

Deblurring images improved correlation and model accuracy.

Abstract

Xerostomia and radiation-induced salivary gland dysfunction remain a common side effect for head-and-neck radiotherapy patients, and attempts have been made to quantify the heterogeneous dose response within parotid glands. Here several models of parotid gland subregional importance are compared with prostate specific membrane antigen (PSMA) positron emission tomography (PET) uptake. PSMA ligands show high concentrations in salivary glands, whose uptake has been previously found to relate to gland functionality. We develop a predictive model for relative importance estimates using PSMA PET and CT radiomic features, and demonstrate a methodology for predicting patient-specific importance deviations from the population. Intra-parotid gland uptake was compared with four regional importance models using 30 [18F]DCFPyL PSMA PET images. A radiomics-based predictive model of population importance was developed using a double cross-validation methodology. Population importance estimates were supplemented using patient-specific radiomic features. Anticorrelative relationships were found to exist between PSMA PET uptake and four independent models of subregional parotid gland importance from the literature. Kernel Ridge Regression with principal component analysis feature selection performed best over test sets (MAE = 0.08), with GLCM features being particularly important. Deblurring PSMA PET images strengthened correlations and improved model performance. This study suggests that regions of relatively low PSMA PET concentration in parotid glands may exhibit relatively high dose-sensitivity. We've demonstrated the utility of PSMA PET radiomic features for predicting relative importance within the parotid glands. PSMA PET appears promising for analyzing salivary gland functionality.