2 The Contribution of Brain Metastases to Neurocognitive Functioning in Patients with Advanced Metastatic Cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».