Locally advanced breast cancer treated with neoadjuvant chemotherapy and adjuvant radiotherapy: a retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: Neoadjuvant chemotherapy (NAC) is increasingly used to treat locally advanced breast cancer (LABC). Improved response to NAC correlates with better survival outcomes. The dual purpose of this study is to report recurrence and survival outcomes for LABC patients treated with NAC, surgery and adjuvant radiotherapy and to correlate these outcomes with tumour response after NAC using multiple response assessment methods. METHODS: All LABC patients treated for curative intent with NAC, surgery, and adjuvant radiotherapy at our institute between January 2009 and December 2014 were included for analysis. NAC was mostly anthracycline and taxane-based; radiotherapy consisted of 50 Gy to the breast/chest wall and regional lymph nodes. Response to NAC was categorized using synoptic pathology reports, modified-RECIST and Chevallier scores. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. RESULTS: The cohort included 103 patients nearly equally divided between Stage II (n = 53) and Stage III (n = 50). Rates of locoregional control (LRC), recurrence-free survival (RFS), and overall survival (OS) were 99, 98, and 100% at 1 year and 89, 69 and 77% at 5 years, respectively. Responses to NAC did not correlate with LRC (p > 0.05) but did correlate with RFS and OS (p < 0.05), except that the Chevallier score did not predict RFS (p = 0.06). Using bivariate Cox modeling tumour size before (p = 0.003) and after (p < 0.001) NAC, stage group (p = 0.05), and response assessed by synoptic pathology (p = 0.05), modified-RECIST (p = 0.001), and Chevallier score (p = 0.015) all predicted for RFS. No factors predicted for LRC. CONCLUSION: Pathologic response by all tested methods correlated with improved survival but were not associated with decreased LRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".