Role of Detection Method in Predicting Breast Cancer Survival: Analysis of Randomized Screening Trials
Bibliographic record
Abstract
BACKGROUND: Screening mammography detects breast cancers earlier than those detected symptomatically, and so mammographically detected breast cancers tend to have better prognoses. The so-called stage shift that results from screen detection is subject to lead-time and length biases, and so earlier detection may not translate into longer survival. We used data from three large breast cancer screening trials--Health Insurance Plan (HIP) of New York and two Canadian National Breast Cancer Screening Studies (CNBSS)--to investigate survival benefits of breast cancer screening beyond stage shift. We also address whether method of detection is an independent prognostic factor in breast cancer. METHODS: The HIP trial randomly assigned approximately 62,000 women to screening and control groups. The two CNBSS trial cohorts CNBSS-1 and CNBSS-2 included a total of 44,970 women in the screening group and 44,961 in the control group. After adjusting for stage and other tumor characteristics in a Cox proportional hazards model, survival distributions were compared by method of breast cancer detection with both univariate and multivariable analyses. All P values are two-sided. RESULTS: Breast cancers detected by screening mammography had a shift in stage distribution to earlier stages (for HIP, P < .001; for CNBSS-1, P = .03; and for CNBSS-2, P < .001). After adjusting for tumor size, lymph node status, and disease stage in a Cox proportional hazards model, method of detection was a statistically significant independent predictor of disease-specific survival. Patients with interval cancers had a 53% (95% confidence interval [CI] = 17% to 100%) greater hazard of death from breast cancer than patients with screen-detected cancers, and patients with cancer in the control groups had a 36% (95% CI = 10% to 68%) greater hazard of death than patients with screen-detected cancer. CONCLUSION: There was an apparent survival benefit beyond stage shift for patients with screen-detected breast cancers compared with patients with breast cancers detected otherwise. Method of detection appears to be an important prognostic factor, even after adjusting for known tumor characteristics. This finding suggests that clinical trialists should routinely collect information about method of detection.
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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 | 0.278 | 0.396 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".