Impact of [18F]FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology

Abstract

Delineation of tumour boundaries is important for quantification of [18F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) studies and for definition of biological target volumes in radiotherapy. Several (semi-)automatic tumour delineation methods have been proposed, but these methods differ substantially in estimating tumour volume and their performance may be affected by imaging parameters. The main purpose of this study was to explore the performance dependence of various (semi-)automatic tumour delineation methods on different imaging parameters, i.e. reconstruction parameters, noise levels and tumour characteristics, and thereby the need for standardization or inter-institute calibration. Six different types of delineation methods were evaluated by assessing accuracy and precision in estimating tumour volume from simulations and phantom experiments. The evaluated conditions were various tumour sizes, iterative reconstruction algorithm settings and image filtering, tumour to background ratios (TBR), noise levels and region growing initializations. The accuracy of all automatic delineation methods was influenced when imaging parameters were varied. The performance of all tumour delineation methods depends on variation of TBR, image resolution and image noise level, and to a lesser extent on number of iterations during image reconstruction or the initialization method of the region generation. For sphere sizes larger than 20 mm diameter a contrast-oriented method provided the most accurate results, on average, over all simulated conditions. For threshold-based methods the accuracy of tumour delineation improved after image denoising/filtering. The accuracy and precision of all studied tumour delineation methods was affected by physiological and imaging parameters. The latter illustrates the need for optimizing imaging parameters and/or for careful calibration and optimization of delineation methods.