To evaluate the serial changes and correlations between readout-segmented technique with navigated phase correction diffusion-weighted MRI (DWI), R  ₂*-MRI and ¹⁸F-FDG positron emission tomography (PET) CT performed before and during radiation therapy (RT) in patients with mucosal primary head and neck cancer.

The mean apparent diffusion coefficient (ADC_mean) from DWI (at b = 50 and 800 s mm⁻²), the mean R  ₂* values derived from T  ₂  ^*-MRI, and PET metabolic parameters, including maximum standardized uptake value (SUV_max), metabolic tumour volume (MTV) and total lesional glycolysis (TLG) were measured for the primary tumour. Spearman correlation coefficients were calculated to evaluate correlations between ADC_mean, R  ₂*, SUV_max, MTV and TLG. A paired t-test was performed to assess the MRI changes and the slope of serial MRI changes during RT.

Pre-treatment scans were performed in 28 patients and mid-treatment scans in 20 patients. No significant correlation was found between ADC_mean and either R  ₂* values or PET parameters. There were significant negative correlations of R  ₂* values with pre-treatment PET parameters but not with mid-RT PET parameters: pre-SUV_max (p = 0.008), pre-MTV (p = 0.006) and pre-TLG (p = 0.008). A significant rise in ADC_mean was found during the first half (p < 0.001) of RT but not in the second half (p = 0.215) of the treatment. There was an increase of the ADC_mean values of 279.4 [95% confidence interval (95% CI): 210–348] in the first half of the treatment (Weeks 0–3). However, during the second-half period of treatment, the mean ADC value (Weeks 3–6) was 24.0 and the 95% CI (−40 to 88) included zero. This suggests that there was no significant change in ADC values during the second half of the treatment.

A significant negative correlation was found between pre-treatment R  ₂*-MRI and PET parameters. DWI appeared to demonstrate potentially predictable changes during RT.

Understanding the correlation and changes that occur with time between potential imaging biomarkers may help us establish the most appropriate biomarkers to consider in future research.