Predicting OncoDX Recurrence Scores With Immunohistochemical Markers
Bibliographic record
Abstract
Recent reports suggest that immunohistochemistry (IHC) markers can be used to give prognostic information in breast cancer that is similar to that contained in the Genomic Health Inc. OncoDX recurrence score (Onco-RS). Our own previous work confirmed that an IHC model based on estrogen receptor (ER), progesterone receptor (PR), and Ki67 predicts 62% of the Onco-RS variability. Other markers used in the Onco-RS include proteins thought to increase tumoral invasive potential, and one such marker is matrix metalloproteinase-11, also called stromelysin 3 (ST3). The goal of this study is to examine the additional value of including ST3 in an IHC-based model that also includes ER, PR, and Ki67 in predicting the Onco-RS, as compared with an IHC model based only on ER, PR, and Ki67 (IHC-RS). The patient population consists of a retrospectively identified cohort of 91 women with ER-positive, HER2neu-negative breast cancer who completed OncoDX testing. Using stepwise multiple regression incorporating Ki67 percentage and semiquantitative ER, PR, and ST3 scores, the ST3 score was not statistically significant.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".