Using FDG-PET to measure early treatment response in head and neck squamous cell carcinoma: quantifying intrinsic variability in order to understand treatment-induced change.

BACKGROUND AND PURPOSE Quantification of both baseline variability and intratreatment change is necessary to optimally incorporate functional imaging into adaptive therapy strategies for HNSCC. Our aim was to define the baseline variability of SUV on FDG-PET scans in patients with head and neck squamous cell carcinoma and to compare it with early treatment-induced SUV change. MATERIALS AND METHODS Patients with American Joint Committee on Cancer stages III-IV HNSCC were imaged with 2 baseline PET/CT scans and a third scan after 1-2 weeks of curative-intent chemoradiation. SUVmax and SUVmean were measured in the primary tumor and most metabolically active nodal metastasis. Repeatability was assessed with Bland-Altman plots. Mean percentage differences (%ΔSUV) in baseline SUVs were compared with intratreatment %ΔSUV. The repeatability coefficient for baseline %ΔSUV was compared with intratreatment %ΔSUV. RESULTS Seventeen patients had double-baseline imaging, and 15 of these patients also had intratreatment scans. Bland-Altman plots showed excellent baseline agreement for nodal metastases SUVmax and SUVmean, but not primary tumor SUVs. The mean baseline %ΔSUV was lowest for SUVmax in nodes (7.6% ± 5.2%) and highest for SUVmax in primary tumor (12.6% ± 9.2%). Corresponding mean intratreatment %ΔSUVmax was 14.5% ± 21.6% for nodes and 15.2% ± 22.4% for primary tumor. The calculated RC for baseline nodal SUVmax and SUVmean were 10% and 16%, respectively. The only patient with intratreatment %ΔSUV above these RCs was 1 of 2 patients with residual disease after CRT. CONCLUSIONS Baseline SUV variability for HNSCC is less than intratreatment change for SUV in nodal disease. Evaluation of early treatment response should be measured quantitatively in nodal disease rather than the primary tumor, and assessment of response should consider intrinsic baseline variability.