2 The Contribution of Brain Metastases to Neurocognitive Functioning in Patients with Advanced Metastatic Cancer
Bibliographic record
Abstract
Objective: Neurocognitive decline is prevalent in patients with metastatic cancers, attributed to various disease, treatment, and individual factors. Whether the presence of brain metastases (BrMets) contributes to neurocognitive decline is unclear. Aims of this study are to examine neurocognitive performance in BrMets patients and compare findings to patients with advanced metastatic cancer without BrMets. Here, we present baseline findings from an ongoing, prospective longitudinal study. Participants and Methods: English-speaking adults with advanced metastatic cancers were recruited from the brain metastases and lung clinics at the Princess Margaret Cancer Centre. Participants completed standardized tests (WTAR, HVLT-R, BVMT-R, COWAT, Trailmaking test, WAIS-IV Digit Span) and questionnaires (FACT-Cog v3, EORTC-QLQ C30 and BN20, PROMIS Depression(8a) and Anxiety(6a)) prior to cranial radiotherapy for those who required it. Test scores were converted to z-scores based on published normative data and averaged to create a composite neurocognitive performance score and domain scores for memory, attention/working memory, processing speed and executive function. Neurocognitive impairment was defined according to International Cancer and Cognition Task Force criteria. Univariate and multivariate regressions were used to identify individual, disease and treatment variables that predict cognitive performance. Results: 76 patients (mean (SD) age: 63.2 (11.7) years; 53% male) with BrMets were included. 61% experienced neurocognitive impairment overall; impairment rates varied across domains (38% memory, 39% executive functioning, 13% attention/working memory, 8% processing speed). BrMets quantity, volume, and location were not associated with neurocognitive performance. Better performance status (ECOG; ß[95%CI];-0.38[-0.70,-0.05], p=0.021), higher premorbid IQ (0.34[0.10,0.58], p=0.005) and greater cognitive concerns (0.02[-3.9e-04,0.04], p=0.051) were associated with better neurocognitive performance in univariate analyses. Only premorbid IQ (0.37[0.14,0.60], p=0.003) and cognitive concerns (0.02[0.0004, 0.03], p=0.05) remained significant in multivariate analysis. We also recruited 31 patients with metastatic non-small cell lung cancer (mNSCLC) with no known BrMets (age: 67.5 (8.3); 32% male) and compared them to the subgroup of BrMets patients in our sample with mNSCLC (N=32; age: 67.8 (11.7); 53% male). We found no differences in impairment rates (BrMets/non-BrMets: Cognitive Composite, 59%/55%; Memory, 31%/32%; Executive Functioning, 35%/29%; Attention/working memory, 16%/13%; Processing speed, 7%/6%; Wilcoxon rank-sum test, all p-value’s > 0.5). The presence or absence of BrMets did not predict neurocognitive performance. Among patients with mNSCLC, higher education (0.11[0.03,0.18], p=0.004) and premorbid IQ (0.36[0.12,0.61], p=0.003), fewer days since primary diagnosis (0.00290[-0.0052,-0.0005], p=0.015) fewer pack-years smoking history (0.01[0.02,-0.001], p=0.027) and greater cognitive concerns (0.02[7e-5,0.04], p=0.045) were associated with better neurocognitive performance in univariate analyses; only premorbid IQ (0.26[0.02,0.51], p=0.04) and cognitive concerns (0.02[0.01,0.04], p=0.02) remained significant in multivariate analysis. Conclusions: Cognitive impairment is prevalent in patients with advanced metastatic cancers, particularly affecting memory and executive functioning. However, 39% of patients in our sample were not impaired in any domain. We found no associations between the presence of BrMets and neurocognitive function in patients with advanced cancers prior to cranial radiation. Premorbid IQ, a proxy for cognitive reserve, was associated with cognitive outcomes in our sample. Our longitudinal study will allow us to identify risk and resilience factors associated with neurocognitive changes in patients with metastatic cancers to better inform therapeutic interventions in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".