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Record W4249568651 · doi:10.1158/1538-7445.am2019-3284

Abstract 3284: Adiposity, muscle mass and delays and dose reductions on adjuvant, taxane-based chemotherapy for breast cancer

2019· article· en· W4249568651 on OpenAlexaff
Elizabeth M. Cespedes Feliciano, Valerie Lee, Wendy Y. Chen, Carla M. Prado, Shlomit Strulov Shachar, Stacey Alexeeff, Bette J. Caan

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineTaxaneBreast cancerInternal medicineChemotherapyCancerOncologyBody surface areaAnthracyclineProportional hazards modelBody mass indexUrologySurgery

Abstract

fetched live from OpenAlex

Abstract Introduction: Low muscle mass and excess adiposity are thought to increase the risk of chemotherapy toxicity, leading to dose reductions or delays. Yet, few studies of body composition and chemotherapy examine patients with breast cancer; prior studies have been small and looked mainly at toxicities. Here, we evaluate whether adiposity or muscle mass and radiodensity (a measure of intramyocellular lipid accumulation) are associated with risk of dose reductions or delays among nonmetastatic breast cancer patients receiving adjuvant taxane-based chemotherapy, and whether dose reductions are associated with cancer-specific survival. Methods: Our study included 1,403 patients with stage II-III breast cancer receiving taxane-based chemotherapy at Kaiser Permanente. We evaluated body composition from clinically-acquired CT scans at diagnosis. We defined dose reductions from infusion records as relative dose intensities <0.85 (delivered v. planned dose) while on taxane therapy, and dose delays as treatments received more than 3 days later than scheduled. We defined neuropathy from diagnosis codes and hematologic toxicities from laboratory values during chemotherapy. Logistic regression models adjusted for age and dosing body surface area (BSA). Cox proportional hazards models for cancer-specific survival adjusted for age, BSA, body composition and tumor characteristics. Results: Mean (standard deviation [SD]) age at diagnosis was 53 (10) years. Higher visceral adiposity was associated with a 20% increased risk of dose reductions and a 17% increased risk of dose delays on taxane-based chemotherapy (odds ratios of 1.20; 95%CI:1.02-1.42 and 1.17; 95%CI:0.99-1.39 per SD, respectively). Higher muscle radiodensity (indicating lower intramyocellular lipid infiltration, i.e., leaner muscle) was associated with a 13% lower risk of dose reductions and a 16% lower risk of dose delays (odds ratios of 0.87; 95%CI:0.75-1.00 and 0.84; 95%CI:0.72-0.98 per SD, respectively). Muscle mass and subcutaneous adiposity were not associated with dose reductions or delays, though lower muscle mass did increase risk of hematologic toxicity. Women who experienced dose reductions on taxane-based chemotherapy had a 37% increased risk of dying from breast cancer relative to those with higher relative dose intensities (hazard ratio 1.37; 95%CI:1.01-1.85; median follow-up 6 years, 203 breast cancer deaths). Conclusions: Excess visceral adiposity and lower muscle radiodensity were associated with dose reductions and delays among breast cancer patients receiving taxane-based chemotherapy, reducing the efficacy of these life-saving therapies: women who experienced dose reductions were at higher risk of dying from breast cancer. Body composition information assessed from clinically-acquired CT scans may help identify patients for supportive interventions to mitigate toxicity. Citation Format: Elizabeth M. Cespedes Feliciano, Valerie Lee, Wendy Y. Chen, Carla M. Prado, Shlomit S. Shachar, Stacey Alexeeff, Bette J. Caan. Adiposity, muscle mass and delays and dose reductions on adjuvant, taxane-based chemotherapy for breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3284.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.405
Teacher spread0.344 · 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
Published2019
Admission routes1
Has abstractyes

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