Can internal mammary lymph nodes irradiation bring survival benefits for breast cancer patients? A systematic review and meta-analysis of 12,705 patients in 12 studies
Bibliographic record
Abstract
Abstract Objective To evaluate the effect of prophylactic irradiation of internal mammary lymph nodes in breast cancer patients. Methods The computer searched PubMed, EMBASE, Web of science, CNKI, Wanfang Medical Network, the Chinese Biomedical Literature Database to find clinical studies on internal mammary lymph node irradiation (IMNI) in breast cancer. The quality of the included literature was evaluated according to the Newcastle–Ottawa scale. Stata14 software was used for meta-analysis. Results A total of 12,705 patients in 12 articles were included for meta-analyzed. Compared with patients who unirradiated internal mammary lymph nodes (non-IMNI), the risk of death for patients after IMNI was reduced by 11% (HR 0.89, 95% CI 0.79–1.00, P = 0.0470); DFS of group mixed N+ patients (high risk group) was significantly improved after IMNI (HR 0.58, 95% CI 0.49–0.69, P < 0.001). Further subgroup analysis shows that compared with non-IMNI, DFS was significantly increased in N1or ypN1 subgroup (HR 0.65, 95% CI 0.49–0.87, P = 0.003) and N2or ypN2 subgroup (HR 0.51, 95% CI 0.37–0.70, P < 0.001) after IMNI, but there was no statistical difference in DFS between the IMNI and non-IMNI groups in N0 subgroup (HR 1.02 95% CI 0.87–1.20, P = 0.794) and N3 or ypN3 subgroup (HR 0.85, 95% CI 0.49–1.45, P = 0.547). No serious incidents were reported in all the included studies, and most of the acute and late side effects were mild and tolerable. Conclusion Under modern radiotherapy techniques, IMNI can safely and effectively bring clinical benefits to N1–2 breast cancer patients, but its role in N0, N3 breast cancer patients remains to be further studied.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".