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

Abstract

Purpose

To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUV _max 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 _max (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 _mean, 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 _max 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 _max thresholds may be appropriate for semi-quantitative analyses with penalised likelihood.