OBJECTIVE

Intratumoral heterogeneity is associated with poor outcomes in head and neck cancer (HNC) patients owing to chemoradiotherapy resistance. [ F]-FDG positron emission tomography (PET) / Magnetic Resonance Imaging (MRI) provides spatial information about tumor mass, allowing intratumor heterogeneity assessment through histogram analysis. However, variability in quantitative PET/MRI parameter measurements could influence their reliability in assessing patient prognosis. Therefore, to use standardized uptake value (SUV) and apparent diffusion coefficient (ADC) parameters for assessing tumor response, this study aimed to measure SUV and ADC's variability and assess their relationship in HNC.

METHODS

First, ADC variability was measured in an in-house diffusion phantom and in five healthy volunteers. The SUV variability was only measured with the NEMA phantom using a clinical imaging protocol. Furthermore, simultaneous PET/MRI data of 11 HNC patients were retrospectively collected from the National Cyclotron and PET center in Chulabhorn Hospital. Tumor contours were manually drawn from PET images by an experienced nuclear medicine radiologist before tumor volume segmentation. Next, SUV and ADC's histogram were used to extract statistic variables of ADC and SUV: mean, median, min, max, skewness, kurtosis, and 5 , 10 , 25 , 50 , 75 , 90 , and 95 percentiles. Finally, the correlation between the statistic variables of ADC and SUV, as well as Metabolic Tumor volume and Total Lesion Glycolysis parameters was assessed using Pearson's correlation.

RESULTS

This pilot study showed that both parameters' maximum coefficient of variation was 13.9% and 9.8% in the phantom and in vivo, respectively. Furthermore, we found a strong and negative correlation between SUV and ADV (r = -0.75, P = 0.01).

CONCLUSION

The SUV and ADC obtained by simultaneous PET/MRI can be potentially used as an imaging biomarker for assessing intratumoral heterogeneity in patients with HNC. The low variability and relationship between SUV and ADC could allow multimodal prediction of tumor response in future studies.