Purpose: Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer (18)F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.
Methods: The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV CT). PVs were visually determined on all PET scans (PV VIS). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV RTL), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV W&C), and a fuzzy locally adaptive Bayesian algorithm (PV FLAB).
Results: Pretreatment PV VIS correlated best with PV FLAB and GTV CT. Correlations with PV RTL and PV W&C were weaker although statistically significant. During treatment, the PV VIS, PV W&C and PV FLAB significant decreased over time with the steepest decline over time for PV FLAB. Among these advanced segmentation methods, PV FLAB was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV W&C and 27 % for PV RTL). A decrease in PV FLAB above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).
Conclusion: In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.