PO-0973 SUVpeak based segmentation to determine lung tumour volume on FDG PET-CT compared with pathology

Abstract

An accurate estimation of lung tumour volume from [18F]-fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) is important for radiotherapy planning and therapy response assessment. Segmentation based on the standard uptake value (SUV) is often used for this purpose. The most common segmentation method is based on the SUVmax, whereby the tumour border is defined by the contour curve at a percentage threshold (ranging from 40-50%) of the maximum intensity voxel within a predefined region of interest. However, since this maximum reflects the value on a single pixel, it may be affected by noise in the image leading to an inaccurate segmentation. The SUVpeak has been proposed as an alternative evaluation method, whereby a uniform averaging filter with a 1 cc kernel is used to evaluate the SUV. Since this method does not rely on a single pixel, it is less likely to be affected by noise and may therefore be a more reliable value to base the segmentation on. The aim of the study was to compare simple SUVpeak and SUVmax based segmentation methods for calculating the lung tumour volume, compared to a pathology ground truth.