An Individual Person Data Meta-Analysis of Preoperative Magnetic Resonance Imaging and Breast Cancer Recurrence
Bibliographic record
Abstract
PURPOSE: There is little consensus regarding preoperative magnetic resonance imaging (MRI) in breast cancer (BC). We examined the association between preoperative MRI and local recurrence (LR) as primary outcome, as well as distant recurrence (DR), in patients with BC. METHODS: An individual person data (IPD) meta-analysis, based on preoperative MRI studies that met predefined eligibility criteria, was performed. Survival analysis (Cox proportional hazards modeling) was used to investigate time to recurrence and to estimate the hazard ratio (HR) for MRI. We modeled the univariable association between LR (or DR) and MRI, and covariates, and fitted multivariable models to estimate adjusted HRs. Sensitivity analysis was based on women who had breast conservation with radiotherapy. RESULTS: Four eligible studies contributed IPD on 3,180 affected breasts in 3,169 subjects (median age, 56.2 years). Eight-year LR-free survival did not differ between the MRI (97%) and no-MRI (95%) goups (P = .87), and the multivariable model showed no significant effect of MRI on LR-free survival: HR for MRI (versus no-MRI) was 0.88 (95% CI, 0.52 to 1.51; P = .65); age, margin status, and tumor grade were associated with LR-free survival (all P < .05). HR for MRI was 0.96 (95% CI, 0.52 to 1.77; P = .90) in sensitivity analysis. Eight-year DR-free survival did not differ between the MRI (89%) and no-MRI (93%) groups (P = .37), and the multivariable model showed no significant effect of MRI on DR-free survival: HR for MRI (v no-MRI) was 1.18 (95% CI, 0.76 to 2.27; P = .48) or 1.31 (95% CI, 0.76 to 2.27; P = .34) in sensitivity analysis. CONCLUSION: Preoperative MRI for staging the cancerous breast does not reduce the risk of LR or DR.
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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".