Mapping glioma’s impact on cognition: Insights from macrostructure, microstructure, and beyond
Bibliographic record
Abstract
Background: Cognitive impairment (CI) significantly impacts the quality of life of glioma patients. The main contributing risk factors include tumor characteristics, treatment-related factors, and their complex interplay. This review explores the role of advanced structural neuroimaging techniques in understanding CI in glioma patients. Methods: A literature search was conducted in PubMed, PsycINFO, and ISI Web of Knowledge using specific keywords. We included studies with advanced magnetic resonance imaging techniques and objective neuropsychological exams. Results: At diagnosis, during the pre-surgery phase, associations between glioma characteristics and cognitive outcomes have been described. Specifically, patients with isocitrate dehydrogenase (IDH)-wild-type gliomas exhibit more adverse cognitive outcomes, accompanied by disruptions in gray (GM) and white matter (WM) networks when compared to IDH-mutant. In addition, pre- and post-surgery imaging analyses highlight the importance of preserving specific WM tracts, such as the inferior longitudinal and arcuate fasciculus, in mitigating verbal memory and language processing decline. Furthermore, examining gliomas in perisylvian regions emphasizes deleterious effects on various cognitive domains. Additionally, it has been suggested that neuroplastic reorganization could serve as a compensatory mechanism against CI. Lastly, a limited number of studies suggest long-term CI linked to GM atrophy and leukoencephalopathy induced by radiotherapy ± chemotherapy in glioma survivors, highlighting the need for improving treatment approaches, particularly for patients with extended survival expectations. Conclusion: This review underscores the need for nuanced understanding and an individual approach in the management of glioma patients. Neuroplastic insights offer clinicians valuable guidance in surgical decision-making and personalized therapeutic approaches thus improving patient outcomes in neuro-oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".