Concurrent Neoadjuvant Chemotherapy and Radiation in Locally Advanced Breast Cancer: Impact on Locoregional Recurrence Rates
Bibliographic record
Abstract
Neoadjuvant chemoradiation therapy (NCRT) is an underutilized treatment in breast cancer but may improve outcomes by impacting the tumor immune microenvironment. The aim of this study was to evaluate NCRT’s impact on recurrence and the role of tumor-infiltrating lymphocytes (TILs) in treatment response. We hypothesized that NCRT reduces recurrence by upregulating TILs. Patients with locally advanced breast cancer (LABC) were treated with NCRT. Stage IIB to III patients with any molecular subtypes were eligible. The patients were matched for age, stage, and molecular subtype by a propensity score to a concurrent cohort receiving standard neoadjuvant chemotherapy (NCT) followed by adjuvant radiation. The objective of this study was to assess the patients in terms of the pathological complete response (pCR), TIL counts prior to and following treatment, and locoregional recurrence. The median follow-up was 7.2 years. Thirty NCRT patients were successfully matched 1:3 to ninety NCT patients. The NCRT cohort had no regional and locoregional recurrences (p = 0.036, (hazard ratio) HR [0.25], 95% confidence interval (CI) [0.06–0.94] and p = 0.013, HR [0.25], 95% CI [0.08–0.76], respectively), compared to 17.8% of the NCT cohort. The NCRT group had significantly more pCRs, and TILs were increased in the post-treatment pCR specimens. NCRT can improve outcomes in LABC patients, with a higher pCR and significantly lower locoregional recurrence/higher recurrence-free survival. Further trials are needed to evaluate the role of NCRT in all breast cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".