Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy

Background and purpose: This study evaluated the use of total lesion glycolysis (TLG) determined by different automatic segmentation algorithms, for early response monitoring in non-small cell lung cancer (NSCLC) patients during concomitant chemoradiotherapy. Materials and methods: Twenty-seven patients with locally advanced NSCLC treated with concomitant chemoradiotherapy underwent F-18-fluorodeoxyglucose (FDG) PET/CT imaging before and in the second week of treatment. Segmentation of the primary tumours and lymph nodes was performed using fixed threshold segmentation at (i) 40% SUVmax (T40), (ii) 50% SUVmax (T50), (iii) relative-threshold-level (RTL), (iv) signal-to-background ratio (SBR), and (v) fuzzy locally adaptive Bayesian (FLAB) segmentation. Association of primary tumour TLG (TLGT), lymph node TLG (TLGLN), summed TLG (TLG(S) = TLG(T) + TLG(LN)), and relative TLG decrease (Delta TLG) with overall -survival (OS) and progression-free survival (PFS) was determined using univariate Cox regression models. Results: Pretreatment TLG(T) was predictive for PFS and OS, irrespective of the segmentation method used. Inclusion of TLG(LN) improved disease and early response assessment, with pretreatment TLG(S) more strongly associated with PFS and OS than TLG(T) for all segmentation algorithms. This was also the case for Delta TLG(S), which was significantly associated with PFS and OS, with the exception of RTL and T40. Conclusions: Delta TLG(S) was significantly associated with PFS and OS, except for RTL and T40. Inclusion of TLG(LN) improves early treatment response monitoring during concomitant chemoradiotherapy with FDG-PET. (C) 2016 Elsevier Ireland Ltd. All rights reserved.