Correlation between Nottingham grade and Oncotype DX score in breast cancer: Implications for cost-effective incorporation of oncotype in the clinic.
Bibliographic record
Abstract
106 Background: Nottingham grading (NG) for breast cancer uses tubule formation (TF), nuclear polymorphism (NP), and mitotic count (MC). The Oncotype Dx recurrence score (RS) is a 21-gene assay that predicts recurrence. Both NG and RS can influence decisions about the use of adjuvant chemotherapy in patients with ER+, HER2-, T1-T2, N0 cancers. The objective was to determine the correlation of overall NG, TF, NP, MC with RS risk group in such patients. This may be useful in guiding the use of Oncotype in specific cases. Methods: 231 patients referred to the BC Cancer Agency between 2007-2011 with ER+, HER2-, T1-T2, N0 breast cancer and an Oncotype were identified. Histologic grading was assessed on the specimen used for Oncotype. Spearman’s correlation coefficients, and 95% confidence intervals (CI) were calculated for the RS risk group versus overall NG, TF, NP, and MC. This study adds to the literature as it is one of the largest cohorts examining this topic and focuses on a HER2- patient population. Results: There was a significant positive moderate correlation between RS and overall grade (Spearman coefficient 0.47, [95%CI: 0.36, 0.56])(Table 1), and RS and MC (Spearman 0.44, [95%CI: 0.33, 0.54]). There was a significant positive weak correlation between RS and TF (Spearman 0.25, [95%CI: 0.13, 0.37]) and RS and NP (Spearman 0.34, [95%CI: 0.22, 0.45]). None of the patients with low overall NG had a high risk RS. Conclusions: Patients with a low NG are unlikely to have a high risk RS (0 in 231 patients) and very few such patients would benefit from the expense of an Oncotype. Overall NG has a better correlation with RS than TF, MC, or NP components alone. [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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".