Early breast cancer response to neoadjuvant chemotherapy: Defining the optimal timing and response rate using clinical tumor measurement.
Bibliographic record
Abstract
121 Background: Breast cancer pathological complete response (pCR) following neoadjuvant therapy (NAT) is associated with better survival in some tumor subtypes. There is interest in identifying methods to increase early prediction of pCR during NAT. A simple and inexpensive technique such as early clinical breast examination has been shown to correlate with pCR during NAT in some studies. However, the optimal timing of the measurement and the best tumor response (TR) rate to predict pCR still need to be defined. Methods: We conducted a retrospective cohort study of patients prospectively enrolled in the following NSABP trials in one academic center (Montreal, Canada): B-40, B-41, FB-AX-003, FB-4, FB-5 and FB-6. Patients with T4 disease or disease progression were excluded. Clinical tumor measurements were recorded before each cycle of NAT. TR was measured as the percentage decrease in the largest tumor diameter. ROC curves for TR measurements at each time point were generated, comparing areas under the curve using the DeLong method. P-value ² 0.05 was considered significant. Results: We analyzed data of 155 patients recruited from Aug. 2005 to May 2011. Results are presented in Table 1. The best time point to predict pCR was after cycle 2. At this time point, a TR of 46% was the best cutoff value to predict for pCR. Among hormone receptor positive (HR+) and HER-2 positive (HER-2+) breast cancer patients, a TR of 47% after cycle 2 was significantly predictive for pCR. These findings were similar using a 50% TR cutoff after 2 cycles. Conclusions: Observing a 50% reduction in largest tumour diameter on clinical breast examination after cycle 2 of NAT is predictive for pCR in HR+ and HER-2+ breast cancer patients. We recommend using this definition of clinical response in future trials evaluating novel methods to improve early prediction of pCR during NAT. [Table: see text]
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".