A simple and reproducible scoring system for EGFR in colorectal cancer: application to prognosis and prediction of response to preoperative brachytherapy
Bibliographic record
Abstract
The aim of this study was to determine the predictive and prognostic value of epidermal growth factor receptor (EGFR) expression in rectal cancers treated with preoperative high-dose rate brachytherapy and in mismatch-repair (MMR)-proficient colorectal cancers (CRCs), respectively. We validate the use of receiver operating characteristic (ROC) curve analysis to select cutoff scores for EGFR overexpression for the end points studied. Immunohistochemistry (IHC) for EGFR was performed on 82 rectal tumour biopsies and 1197 MMR-proficient CRCs using a tissue microarray. Immunoreactivity was scored as the percentage of positive tumour cells by three pathologists and the inter-observer reliability was assessed. ROC curve-derived cutoffs were used to analyse the association of EGFR overexpression, tumour response and several clinicopathological features including survival. The scoring method was found to be reproducible in rectal cancer biopsies and CRCs. The selected cutoff scores from ROC curve analysis for each clinicopathological feature were highly consistent among pathologists. EGFR overexpression was associated with response to radiotherapy (P-value <0.001) and with worse survival time (P-value <0.001). In multivariate analysis, EGFR overexpression was independently associated with adverse prognosis (P-value <0.001). Epidermal growth factor receptor is a predictive marker of response to preoperative radiotherapy and an independent adverse prognostic factor CRC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".