Evaluation of applying IHC4 as a prognostic model in the translational study of Intergroup Exemestane Study (IES): PathIES
Bibliographic record
Abstract
BACKGROUND: Intergroup Exemestane Study (IES) was a randomised study that showed a survival benefit of switching adjuvant endocrine therapy after 2-3 years from tamoxifen to exemestane. This PathIES aimed to assess the role of immunohistochemical (IHC)4 score in determining the relative sensitivity to either tamoxifen or sequential treatment with tamoxifen and exemestane. PATIENTS AND METHODS: Primary tumour samples were available for 1274 patients (27% of IES population). Only patients for whom the IHC4 score could be calculated (based on oestrogen receptor, progesterone receptor, HER2 and Ki67) were included in this analysis (N = 430 patients). The clinical score (C) was based on age, grade, tumour size and nodal status. The association of clinicopathological parameters, IHC4(+C) scores and treatment effect with time to distant recurrence-free survival (TTDR) was assessed in univariable and multivariable Cox regression analyses. A modified clinical score (PathIEscore) (N = 350) was also estimated. RESULTS: Our results confirm the prognostic importance of the original IHC4, alone and in conjunction with clinical scores, but no significant difference with treatment effects was observed. The combined IHC4 + Clinical PathIES score was prognostic for TTDR (P < 0.001) with a hazard ratio (HR) of 5.54 (95% CI 1.29-23.70) for a change from 1st quartile (Q1) to Q1-Q3 and HR of 15.54 (95% CI 3.70-65.24) for a change from Q1 to Q4. CONCLUSION: In the PathIES population, the IHC4 score is useful in predicting long-term relapse in patients who remain disease-free after 2-3 years. This is a first trial to suggest the extending use of IHC4+C score for prognostic indication for patients who have switched endocrine therapies at 2-3 years and who remain disease-free after 2-3 years.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".