Correlation between cancer-related cognitive impairment and resting cerebral glucose metabolism in patients with ovarian cancer
Bibliographic record
Abstract
BackgroundAn increasing number of research have applied neuroimaging techniques to explore the potential neurobiological mechanism of Cancer-related cognitive impairment (CRCI).PurposeTo explore the correlation between resting brain glucose metabolism and CRCI using 18F-FDG PET/CT in ovarian cancer (OC) patients.MethodsFrom December 2021 to March 2022, 38 patients with OC were selected as the study group, and 38 healthy women of the same age (±1 year) who underwent routine physical examination using PET/CT were selected as the control group. Patients received further assessment with the Montreal Cognitive Assessment Scale (MoCA) and Perceived Deficit Questionnaire (PDQ). Independent sample t-test and Spearman correlation were conducted for data analysis.ResultsThe resting brain glucose metabolism in the OC group was significantly lower than in the healthy controls. 60.52 % patients had neuropsychological impairment and retrospective memory were the most serious perceived cognitive impairments. The resting brain glucose metabolism in OC patients did not significantly correlate with neuropsychological performance but had significant positive correlation with subjective cognitive evaluation.DiscussionResting glucose metabolism was low in OC patients and associated with subjective cognitive impairment but not objective neuropsychological test results. 18F-FDG PET/CT can be used to evaluate brain function in OC patients and provide reliable imaging indicators for early recognition of and intervention for changes in cognitive function.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".