Selecting immunohistochemical cut-off scores for novel biomarkers of progression and survival in colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Cut-off scores for determining positivity of biomarkers detected by immunohistochemistry are often set arbitrarily and vary between reports. AIMS: To evaluate the performance of receiver operating characteristic (ROC) curve analysis in determining clinically important cut-off scores for a novel tumour marker, the receptor for hyaluronic acid mediated motility (RHAMM), and show the reproducibility of the selected cut-off scores in 1197 mismatch-repair (MMR) proficient colorectal cancers (CRC). METHODS: Immunohistochemistry for RHAMM was performed using a tissue microarray of 1197 MMR-proficient CRC. Immunoreactivity was scored using a semi-quantitative scoring method by evaluating the percentage of positive tumour cells. ROC curve analysis was performed for T stage, N stage, tumour grade, vascular invasion and survival. The score with the shortest distance from the curve to the point with both maximum sensitivity and specificity, i.e. the point (0.0, 1.0), was selected as the cut-off score leading to the greatest number of tumours correctly classified as having or not having the clinical outcome. In order to determine the reliability of the selected cut-off scores, 100 bootstrapped replications were performed to resample the data. RESULTS: The cut-off score for T stage, N stage, tumour grade and vascular invasion was 100% and that for survival 90%. The most frequently selected cut-off score from the 100 resamples was also 100% for T stage, N stage, tumour grade, and vascular invasion and 90% for survival. CONCLUSIONS: ROC curve analysis can be used as an alternative method in the selection and validation of cut-off scores for determining the clinically relevant threshold for immunohistochemical tumour positivity.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".