Body Mass Index at Diagnosis and Breast Cancer Survival Prognosis in Clinical Trial Populations from NRG Oncology/NSABP B-30, B-31, B-34, and B-38
Bibliographic record
Abstract
BACKGROUND: Body mass index (BMI) has been associated with breast cancer outcomes. However, few studies used clinical trial settings where treatments and outcomes are consistently evaluated and documented. There are also limited data assessing how patient/disease characteristics and treatment may alter the BMI-breast cancer association. METHODS: We evaluated 15,538 breast cancer participants from four NSABP protocols. B-34 studied early-stage breast cancer patients (N = 3,311); B-30 and B-38 included node-positive breast cancer patients (N = 5,265 and 4,860); and B-31 studied node-positive and HER2-positive breast cancer patients (N = 2,102). We used Cox proportional hazards regression to calculate adjusted hazards ratios (HR) for risk of death and recurrence, and conducted separate analyses by estrogen receptor (ER) status and treatment group. RESULTS: In B-30, increased BMI was significantly related to survival. Compared with BMI < 25, HRs were 1.04 for BMI 25 to 29.9 and 1.18 for BMI ≥ 30 (P = 0.02). Separate analyses indicated the significant relationship was only in ER-positive disease (P = 0.002) and the subgroup treated with doxorubicin/cyclophosphamide (P = 0.005). There were no significant trends across BMI for the other three trials. Similar results were found for recurrence. Increased BMI was significantly related to recurrence in B-30 (P = 0.03); and the significant relationship was only in ER-positive breast cancers (P = 0.001). Recurrence was also significant among ER-positive disease in B-38 (P = 0.03). CONCLUSIONS: In our investigation, we did not find a consistent relationship between BMI at diagnosis and breast cancer recurrence or death. IMPACT: This work demonstrates that the heterogeneity of breast cancer between different breast cancer populations and the different therapies used to treat them may modify any association that exists between BMI and breast cancer outcome.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".