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Background: The increasing incidence of brain metastases (BMs) and improved survival rates underscore the necessity to investigate the effects of treatments on individuals. The aim of this study was to evaluate the individual trajectories of subjective and objective cognitive performance after radiotherapy in patients with BMs. Methods: The study population consisted of adult patients with BMs referred for radiotherapy. A semi-structured interview and comprehensive neurocognitive assessment (NCA) were used to assess both subjective and objective cognitive performance before, 3 months and ≥ 11 months after radiotherapy. Reliable change indices were used to identify individual, clinically meaningful changes. Results: Thirty-six patients completed the 3-month follow-up, and 14 patients completed the ≥ 11-months follow-up. Depending on the domain, subjective cognitive decline was reported by 11-22% of patients. In total, 50% of patients reported subjective decline in at least one cognitive domain. Intracranial progression 3 months postradiotherapy was a risk-factor for self-reported deterioration (P = .031). Objective changes were observed across all domains, with a particular vulnerability for decline in memory at 3 months postradiotherapy. The majority of patients (81%) experienced both a deterioration as well as improvement (eg, mixed response) in objective cognitive functioning. Results were similar for the long-term follow-up (3 to ≥11 months). No risk factors for objective cognitive change 3 months postradiotherapy were identified. Conclusions: Our study revealed that the majority of patients with BMs will show a mixed cognitive response following radiotherapy, reflecting the complex impact. This underscores the importance of patient-tailored NCAs 3 months postradiotherapy to guide optimal rehabilitation strategies.

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OBJECTIVE: Patients with brain metastases (BrMs) are a heterogeneous population, with almost 50% experiencing cognitive impairment before brain radiotherapy. Defining pre-radiotherapy cognitive profiles will aid in understanding of the cognitive vulnerabilities and offer valuable insight and guidance for tailoring interventions. METHODS: The study population consisted of 58 adult patients with BrMs referred for radiotherapy. A semi-structured interview and comprehensive battery including 10 neuropsychological tests were used to assess subjective and objective cognitive performance prior to radiotherapy. RESULTS: A majority (69%) of patients report decline in cognitive performance compared to their premorbid level (i.e. pre-cancer). Objective testing revealed memory (52%), processing speed (33%) and emotion recognition (29%) deficits were most frequent. 21% of patients had no cognitive deficits while 55% had deficits (-1.5SD) in at least two cognitive domains. Hierarchical cluster analysis based on patient deficit profiles identified four clusters: (I) no or limited cognitive deficits selectively restricted to processing speed or executive function, (II) psychomotor speed deficits, (III) memory deficits and (IV) extensive cognitive deficits including memory. No patient or clinical-related (e.g. age, number of BrMs, previous treatment) differences were found between clusters. CONCLUSIONS: Patterns of cognitive performance in patients with BrMs are heterogeneous, with most experiencing at least some degree of neurocognitive dysfunction. We identified four meaningful cognitive clusters. Stability of these clusters over time and in different samples should be assessed to advance understanding of the cognitive vulnerability of this patient population.

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BACKGROUND AND PURPOSE: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. MATERIALS AND METHODS: BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients' pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. RESULTS: The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54-3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. CONCLUSION: We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.

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OBJECTIVE: To provide an overview of clinical and MRI characteristics of the different variants of the leukodystrophy megalencephalic leukoencephalopathy with subcortical cysts (MLC) and identify possible differentiating features. METHODS: We performed an international multi-institutional, cross-sectional observational study of the clinical and MRI characteristics in patients with genetically confirmed MLC. Clinical information was obtained by questionnaires for physicians and retrospective chart review. RESULTS: We included 204 patients with classic MLC, 187 of whom had recessive mutations in MLC1 (MLC1 variant) and 17 in GLIALCAM (MLC2A variant) and 38 patients with remitting MLC caused by dominant GLIALCAM mutations (MLC2B variant). We observed a relatively wide variability in neurologic disability among patients with classic MLC. No clinical differences could be identified between patients with MLC1 and MLC2A. Patients with MLC2B invariably had a milder phenotype with preservation of motor function, while intellectual disability and autism were relatively frequent. Systematic MRI review revealed no MRI features that distinguish between MLC1 and MLC2A. Radiologic improvement was observed in all patients with MLC2B and also in 2 patients with MLC1. In MRIs obtained in the early disease stage, absence of signal abnormalities of the posterior limb of the internal capsule and cerebellar white matter and presence of only rarefied subcortical white matter instead of true subcortical cysts were suggestive of MLC2B. CONCLUSION: Clinical and MRI features did not distinguish between classic MLC with MLC1 or GLIALCAM mutations. Absence of signal abnormalities of the internal capsule and cerebellar white matter are MRI findings that point to the remitting phenotype.

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