Locoregional Treatment for Early-Stage Breast Cancer: Current Status and Future Perspectives
Bibliographic record
Abstract
BACKGROUND: The locoregional recurrence of breast cancer has been reduced due to the multidisciplinary approach of breast surgery, systemic therapy and radiation. Early detection and better surgical techniques contribute to an improvement in breast cancer outcomes. PURPOSE OF REVIEW: The purpose of this review is to have an overview and summary of the current evidence behind the current approaches to the locoregional treatment of breast cancer and to discuss its future direction. SUMMARY: With improved surgical techniques and the use of a more effective neoadjuvant systemic therapy, including checkpoint inhibitors and dual HER2-directed therapies that lead to a higher frequency of pathologic complete responses and advances in adjuvant radiation therapy, breast cancer patients are experiencing better locoregional control and reduced local and systemic recurrence. De-escalation in surgery has not only improved the quality of life in the majority of breast cancer patients, but also maintained the low risk of recurrence. There are ongoing clinical trials to optimize radiation therapy in breast cancer. More modern radiation technologies are evolving to improve the patient outcome and reduce radiation toxicities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".