Neoadjuvant therapy (NAT) for breast cancer at the Odette Cancer Centre: What have we been doing and how do we compare?
Bibliographic record
Abstract
131 Background: NAT is standard of care for locally advanced breast cancer (LABC). Early breast cancer (EBC) may also benefit, particularly certain subtypes, such as HER2-enriched or basal- like. We developed a clinical care pathway to standardize the assessment and treatment of LABC and higher-risk EBC at the Odette Cancer Centre (OCC). Purpose: Evaluate treatment patterns within the LABC/NAT program at the OCC, and to determine if these patterns changed after the introduction of a clinical care pathway. This study also aimed to evaluate treatment efficacy (pCR rates) by NAT regimen and breast cancer phenotype, as compared to the literature. Methods: Retrospective chart review of patients treated with NAT at the OCC from January 2008 to March 2012. Results: 158 patients were reviewed. Median age at diagnosis was 50 years (28-85), and median tumor size was 7 cm (0-21). 74% were stage IIB-IIIB, and 11.2% were considered non-LABC (or EBC). 65.8% of breast cancers were estrogen receptor positive (ER+), and 29.7% were HER-2 positive (HER2+). 96% of NAT chemotherapy regimens were anthracycline and taxane-based, and trastuzumab was given to 29.7% of HER-2 positive cases. pCR was achieved in 20.9% of all cases and this did not vary by type of chemotherapy regimen; pCR rates did vary by breast cancer phenotype, with 6.25%, 57.5% and 25% pCR achieved for ER+, HER2+ and triple negative (TN) cancers, respectively. Median follow-up was 13 months (0-46), and overall DFS was 90%; survival did not vary by NAT regimen or pCR, but did vary by phenotype, whereby DFS was 100% for HER-2 positive, 90% for ER+, and 78% for TN cancers. There was no significant difference found in types of patients, NAT regimen, or pCR rates before and after the pathway was introduced. Conclusions: Practice patterns at the OCC in NAT for breast cancer remained unchanged before and after development of a clinical care pathway, which was meant to standardize the approach to and to possibly increase the use of NAT from beyond LABC to higher-risk EBC. This is likely due to limited pathway dissemination, but other factors contributing to this should be evaluated. pCR rates by cancer phenotype were similar to those found in the literature.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".