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).

METHODS: A systematic search was performed in the PubMed, Scopus, and Web of Science (WoS) databases in June 2022. Patients with head and neck cancer treated with radiotherapy and periodic rs-fMRI assessments were included. A meta-analysis was performed to determine the potential of rs-fMRI for detecting brain changes.

METHODS: Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. 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), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features.

RESULTS: Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919).

CONCLUSION: DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments.

METHODS: PubMed and Scopus databases were searched based on PRISMA guideline to determine studies focusing on changes following NPC RT.

RESULTS: Eleven studies fulfilled the inclusion criteria. Microstructural changes occur most consistently in the temporal region. The changes were correlated with latency in seven studies; fractional anisotropy (FA) and gray matter (GM) volume remained low even after a longer period following RT and areas beyond irradiation site with reduced FA and GM measures. For dosage, only one study showed correlation, thus requiring further investigations.