Impact of Point Spread Function Reconstruction on Thoracic Lymph Node Staging With 18F-FDG PET/CT in Non–Small Cell Lung Cancer

Abstract

The aim of the present study was to evaluate the impact of point spread function (PSF) reconstruction on quantitative values and diagnostic accuracy of FDG PET/CT for nodal staging in non-small cell lung cancer. Fifty-eight consecutive PET/CT examinations were reconstructed with both ordered subset expectation maximization (OSEM) and PSF algorithms. Two readers independently performed a randomized blinded review of PET/CT examinations and gave a nodal status (N0, N1, N2, or N3) to each PET data set. When discordant, a consensus was reached with a third reader. Sensitivity, specificity, positive and negative predictive values (NPV), and positive and negative likelihood ratios (LRs) were assessed and compared using a McNemar test. All PET data sets were then independently analyzed to extract quantitative PET values in 208 nodes and compare them using Bland-Altman analysis. Bland-Altman analysis showed that, on average, PSF reconstruction increased SUVmax, SUVmean, and node/background ratios by 48%, 28%, and 27%, respectively. This increase was more marked for nodes less than 1 cm than for nodes 1 cm or greater (P < 0.0001 for SUVmax, SUVmean, and node/background ratios). Point spread function PET had higher sensitivity (97%) and NPV (92%) than OSEM PET (78% and 57%, respectively; P = 0.01 and P = 0.04, respectively). Negative LR was 0.04 for PSF PET and 0.31 for OSEM PET. By improving activity recovery, especially for nonenlarged nodes, PSF significantly improves the sensitivity, NPV, and negative LR of FDG-PET for nodal staging in non-small cell lung cancer. These data suggest that preoperative invasive nodal staging may be omitted in the case of a negative PSF FDG-PET/CT.