Remote assessment of cognition and quality of life following radiotherapy for glioma: deep-learning-based predictive models and MRI correlates

BACKGROUND: Glioma irradiation often unavoidably damages the brain volume and affects cognition. This study aims to evaluate the relationship of remote cognitive assessments in determining cognitive impairment of irradiated glioma patients in relation to the quality of life and MRI changes.

METHODS: Thirty patients (16-76 aged) with two imaging (pre- and post-RT) and completed cognitive assessments were recruited. Cerebellum, right and left temporal lobes, corpus callosum, amygdala and spinal cord were delineated and their dosimetry parameters were collected. Cognitive assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE)). Regression models and deep neural network (DNN) were used to evaluate the relationship between brain volume, cognition and treatment dose in patients.

RESULTS: Cognitive assessments were highly inter-correlated (r > 0.9) and impairment was shown between pre- and post-RT findings. Brain volume atrophy was shown post-RT, and cognitive impairments were correlated with radiotherapy-associated volume atrophy and dose-dependent in the left temporal lobe, corpus callosum, cerebellum and amygdala. DNN showed a good area under the curve for cognitive prediction; TICS (0.952), T-MoCA (0.909) and Tele-MACE (0.822).

CONCLUSIONS: Cognition can be evaluated remotely in which radiotherapy-related brain injury is dose-dependent and volume-dependent. Prediction models can assist in the early identification of patients at risk for neurocognitive decline following RT for glioma, thus facilitating potential treatment interventions.