Application of neo-bioscore to a real-world cohort: Potential implications for patient selection, for escalation, and de-escalation of therapy.
Bibliographic record
Abstract
e19094 Background: Recent studies have demonstrated that escalation of therapy for patients with residual disease after neoadjuvant therapy for breast cancer may result in better long-term outcomes. However, refining patient selection based on prognosis has not been widely adopted. The Neo-Bioscore is a validated independent prognostic tool that can be readily applied clinically. The utility of this tool to predict the expected magnitude of benefit from escalation of therapy for ER+, HER2+ and TNBC disease has not yet been explored. Methods: A retrospective review of prospectively collected data from May 2012-May 2019 of patients treated with neoadjuvant chemotherapy followed by surgery was undertaken. The Neo-Bioscore was calculated for each patient. Kaplan-Meier method was used to generate disease-specific survival (DSS) curves, and 5-year DSS rates were estimated. These estimates were compared to those predicted by the original validation cohort. The potential benefit of further adjuvant therapy was extrapolated from data available in the published literature. Results: A total of 471 patients were included in this study; all were women, and the median age was 56 (range 24-88). 324 had residual disease at the time of surgery (68%). Median follow up from diagnosis was 4.4 years. 5-year DSS for each Neo-Bioscore category is seen in the Table below. This correlated with the predicted DSS with a correlation coefficient of X. For patients with residual disease, utilization of adjuvant capecitabine for those with ER+ or TNBC and utilization of adjuvant TDM-1 for those with Her2+ disease would have minimal impact on DSS in those with Neo-Bioscore of 0-2, modest benefit for those with Neo-Bioscore of 3-4, and would be of high benefit for those with Neo-Bioscore of 5 or higher. Conclusions: Neo-Bioscore is a tool that can be readily applied to a real-world cohort without additional pathological information, with good correlation to the predicted outcomes in validation studies. The impact and utility of the Neo-Bioscore to stratify patients for selection of adjuvant therapy will need to be validated, but may help better inform treatment in resource constrained environments. [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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".