MRI-based correlates of brain changes and quality of life outcomes following radiotherapy for head and neck cancer.

PURPOSE: Radiotherapy (RT) for brain-related tumours can impact multiple neural structures responsible for cognitive, emotional, physical, and social functioning, which in turn may influence a patient’s quality of life (QoL). This study aimed to determine the demographic, dosimetric, and MRI-derived morphological factors associated with reduced QoL among patients following RT.

METHODS

One hundred patients between the ages of 20 and 76 years who received RT and had both pre-RT and post-RT (6–12 months) MRI scans, along with completed QoL assessments (EORTC QLQ-C30 and HRQoL), were included. Regions of interest (ROIs)—comprising the hippocampus, temporal lobes, cerebellum, and thalamus—were manually delineated, and relevant dosimetric parameters were extracted. QoL assessments were administered post-RT via telephone interviews. Spearman correlation and Bayesian analyses were performed to examine associations between QoL domains, demographic factors, dosimetry, and MRI-based volumetric changes.

RESULTS

A significant negative correlation was observed between QoL and treatment dose, with both mean and maximum doses showing associations with lower global QoL (Dmean r = − 0.211, p = 0.035; Dmax r = − 0.229, p = 0.022). Multiple QoL domains, including general health status, physical, emotional, cognitive, and social functioning, demonstrated dose-dependent decline (p < 0.05). QoL was also influenced by gender and cancer stage. Bayesian estimation identified demographic factors, dosimetric parameters, and morphological volume changes—particularly in the hippocampus, bilateral temporal lobes, cerebellum, and thalamus—as significant predictors of post-RT QoL.

CONCLUSION

Post-RT QoL appears to reflect a combined influence of demographic characteristics, tumour stage, radiation dose, and MRI-detected structural alterations. Integration of routine neuroimaging with dosimetric optimisation may improve early identification of at-risk patients and support personalised treatment planning.