18F-FDG PET/CT-based gross tumor volume definition for radiotherapy in head and neck Cancer: a correlation study between suitable uptake value threshold and tumor parameters

Abstract

BackgroundTo define a suitable threshold setting for gross tumor volume (GTV) when using 18Fluoro-deoxyglucose positron emission tomography and computed tomogram (PET/CT) for radiotherapy planning in head and neck cancer (HNC).MethodsFifteen HNC patients prospectively received PET/CT simulation for their radiation treatment planning. Biological target volume (BTV) was derived from PET/CT-based GTV of the primary tumor. The BTVs were defined as the isodensity volumes when adjusting different percentage of the maximal standardized uptake value (SUVmax), excluding any artifact from surrounding normal tissues. CT-based primary GTV (C-pGTV) that had been previously defined by radiation oncologists was compared with the BTV. Suitable threshold level (sTL) could be determined when BTV value and its morphology using a certain threshold level was observed to be the best fitness of the C-pGTV. Suitable standardized uptake value (sSUV) was calculated as the sTL multiplied by the SUVmax.ResultsOur result demonstrated no single sTL or sSUV method could achieve an optimized volumetric match with the C-pGTV. The sTL was 13% to 27% (mean, 19%), whereas the sSUV was 1.64 to 3.98 (mean, 2.46). The sTL was inversely correlated with the SUVmax [sTL = -0.1004 Ln (SUVmax) + 0.4464; R2 = 0.81]. The sSUV showed a linear correlation with the SUVmax (sSUV = 0.0842 SUVmax + 1.248; R2 = 0.89). The sTL was not associated with the value of C-pGTVs.ConclusionIn PET/CT-based BTV for HNC, a suitable threshold or SUV level can be established by correlating with SUVmax rather than using a fixed threshold.

Use of pretreatment metabolic tumour volumes to predict the outcome of pharyngeal cancer treated by definitive radiotherapy

“…Our previous investigation showed that adaptive thresholds could obtain a better match with the presumed tumour volumes [19]. This finding may account for the superior predictive ability of MTV2.5 over MTV40 % or MTV50 %, because the latter two MTVs use a fixed percentage of the local maximum intensity.…”

“…They reconfirmed the allocated point of the SUV max within the tumours. This procedure has been described in our previous report [19].…”

Can adaptive threshold-based metabolic tumor volume (MTV) and lean body mass corrected standard uptake value (SUL) predict prognosis in head and neck cancer patients treated with definitive radiotherapy/chemoradiotherapy?

“…However the SUV of 2.5 is an arbitrary cut-off value, and its use to differentiate malignant lesions is highly questionable especially in head and neck cancer because of high physiologic FDG uptake in surrounding muscular tissue [1,12]. The studies which used a fixed threshold of 40% and 50% of the maximum intratumoral FDG activity for MTV also stated that fixed threshold method was insufficient to define the MTV clearly [12][13][14][15]. Schinagl and associates used five methods for tumor delineation on FDG-PET in 78 patients with head and neck cancer and compared these with CT-based delineation [12].…”

“…In the study by Hatt et al, a fixed threshold of 42% of the maximum resulted in the largest variability of the segmented functional volumes (±15-60%) whereas a fixed threshold of 50% was closer in terms of variability (±20%) [14]. Kao and associates wanted to define a suitable threshold setting for gross tumor volume when using PET/CT for RT planning in head and neck cancer patients [15]. Their results demonstrated that the suitable threshold level was inversely correlated with SUV max .…”

A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study

“…According to a comprehensive review of segmentation algorithms in PET imaging [5], four segmentation methodology categories can be identified. Image thresholding methods are the most widely used due to their simplicity to implement but they are too sensitive to image noise and heterogeneity [6]. Variational approaches based on gradient differences between target and background regions are mathematically efficient but sensitive to image noise and subject to numerical fluctuation [7].…”