Abstract P6-13-04: IMPACt trial: MammaPrint and BluePrint molecular subtyping guide treatment decisions in breast cancer
Bibliographic record
Abstract
Abstract Background: IMPACt is a prospective, case-only study to measure the effect of MammaPrint (MP) and BluePrint (BP) on treatment decisions in breast cancer patients. Here, we report the results of the primary objective in women aged ≥18 years with histologically proven invasive stage I-II, hormone receptor (HR) positive, and HER2-negative breast cancer. Methods: The study included 369 women from 18 US institutions. The recommended treatment plan was captured before and after receiving results for MP and BP. Treatment was started after obtaining results. In addition to the effect of results on physician treatment decisions involving chemotherapy (CT) and physician confidence, the distribution of MP High Risk (HR) and Low Risk (LR) patients was also evaluated. Results: MP classified patients to 62% (n=228) LR and 38% (n=141) HR. Treatment decisions were changed for 25% (n=92) of women after receiving MP and BP results. Of the LR patients initially prescribed CT, 68% (45/66) had CT removed from their treatment recommendation. Of the HR patients who initially were not prescribed CT, 66% (42/64) had CT added. Overall, 89% (202/228) of LR patients did not receive CT, and likewise 84% (119/141) of HR patients did receive CT after receiving MP. Among those who did not change treatment (n=277), 68% of physicians reported having greater confidence in their prescribed therapy. Conclusions: The IMPACt trial shows MP generates a 25% overall treatment change in clinical practice. The highest impact is for women with LR results, where 68% are spared chemotherapy in favor of endocrine therapy alone. Additionally, 73% of physicians report having higher confidence in treatment decisions for their patient after MP. Table 1: Treatment changesTreatment Decision Pre- to Post-MPMP HRMP LRTotalCT to CT772198no CT tp CT42547CT to no CT04545no CT to no CT22157179Total141228369 Citation Format: Soliman H, Rehmus E, Shah V, Srkalovic G, Mahtani R, Levine E, Mavromatis B, Srinivasiah J, Kassar M, Gabordi R, Yoder E, Qamar R, Audeh W, IMPACt Investigators Group I. IMPACt trial: MammaPrint and BluePrint molecular subtyping guide treatment decisions in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-13-04.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".