18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction.

Purpose: To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUV when evaluating mediastinal nodal disease in non-small cell lung cancer (NSCLC) compared to ordered subset expectation maximum (OSEM) reconstruction.

Materials And Methods: 18F-FDG PET/CT scans for NSCLC staging in 47 patients (112 nodal stations with histopathological confirmation) were reconstructed using BPL and compared to OSEM. Node and multiple background SUV parameters were analysed semi-quantitatively and visually.

Results: Comparing BPL to OSEM, there were significant increases in SUV (mean 3.2-4.0, p<0.0001), SBR (mean 2.2-2.6, p<0.0001) and SNR (mean 27.7-40.9, p<0.0001). Mean background SNR on OSEM was 10.4 (range 7.6-14.0), increasing to 12.4 (range 8.2-16.7, p<0.0001). Changes in background SUVs were minimal (largest mean difference 0.17 for liver SUV, p<0.001). There was no significant difference between either algorithm on receiver operating characteristic analysis (p=0.26), although on visual analysis, there was an increase in sensitivity and small decrease in specificity and accuracy on BPL.

Conclusion: BPL increases SBR, SNR and SUV of mediastinal nodes in NSCLC compared to OSEM, but did not improve the accuracy for determining nodal involvement.

Key Points: • Penalised likelihood PET reconstruction was applied for assessing mediastinal nodes in NSCLC. • The new reconstruction generated significant increases in signal-to-background, signal-to-noise and SUVmax. • This led to an improvement in visual sensitivity using the new algorithm. • Higher SUV thresholds may be appropriate for semi-quantitative analyses with penalised likelihood.