RESUMO
AIM: 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. PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
