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Abstract S6-3: Neurocognitive impact in adjuvant chemotherapy for breast cancer linked to fatigue: A Prospective functional MRI study

2012· article· en· W2048873407 on OpenAlexaff
Bernadine Cimprich, DF Hayes, MK Askren, MS Jung, M. G. Berman, Lynn Ossher, Barbara Therrien, PA Reuter-Lorenz, M Zhang, Scott Peltier, DC Noll

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineNeurocognitiveBreast cancerInternal medicineChemotherapyProspective cohort studyOncologyMagnetic resonance imagingRegimenChemotherapy regimenCognitionCancerRadiation therapyRadiologyPsychiatry

Abstract

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Abstract Background: Our previous research showed evidence of compromised cognitive function prior to adjuvant chemotherapy for breast cancer, with fatigue as a contributory factor. Fatigue is a common symptom reported by women treated for breast cancer, yet its association with neurocognitive function has not been systematically examined. In this prospective study, we examined possible alterations in neurocognitive responses, namely, working memory, from pre- to post- adjuvant treatment during functional magnetic resonance imaging (fMRI) and further investigated whether early fatigue might be linked to cognitive alterations over time. Methods: Women treated with either adjuvant chemotherapy (anthracyline-based combination regimen, n=29) or radiotherapy (n = 37) for localized breast cancer (Stages 0-IIIa) and age-matched healthy controls (n = 32) were enrolled. Participants performed a verbal working memory task (VWMT) with varying levels of demand for cognitive control during fMRI scanning and provided self-reports of fatigue (FACT-F) at two time points coincident with pre- and one-month post chemotherapy assessments. Imaging data were analyzed with general linear models using SPM5; comparative statistics were used to determine group differences, and correlational analyses addressed relationships of fatigue and neurocognitive measures. Findings: The chemotherapy group reported significantly greater severity of fatigue (p < .05) and performed less accurately on the VWMT both pre- and one-month post-treatment than the other groups. Greater fatigue was correlated with poorer performance on the VWMT at both time points across groups, with stronger correlation post-treatment (r = −.22, p = .03). A 2 time-point (pre- vs. post-treatment) × 2 group (chemotherapy vs. controls) × 2 demand-level contrasts (high minus low vs. medium minus low) analytic model showed a significant group × time interaction (p < .05), mainly due to lower pre-treatment activation in an area of the prefrontal cortex supporting working memory, the anatomical left inferior frontal gyrus (LiFG), at higher task demand in the chemotherapy group. The radiotherapy group scored between the other two groups with intermediate activation of those contrasts. Of interest, lower pre-treatment activation in the LiFG in the high-low demand contrast predicted severity of fatigue across all participants at the post-treatment assessment (r = −.27, p < .01), linking early compromise in neurocognitive performance with greater fatigue over time. Discussion: Neurocognitive alterations during a working memory task and greater fatigue were evident before any adjuvant chemotherapy for breast cancer. Notably, functional alterations in working memory processes were evident with fMRI before adjuvant chemotherapy and predicted severity of post-treatment fatigue. Importantly, across all participants, greater fatigue over time was correlated with reduced cognitive performance. Taken together, these findings indicate that pre-treatment neurocognitive compromise and fatigue are key contributors to the cognitive impact often attributed solely to chemotherapy. Early therapeutic interventions targeting fatigue may improve cognitive function and reduce the distress of “chemo brain” throughout the course of adjuvant treatment. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr S6-3.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.126
GPT teacher head0.479
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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