Immunohistochemical markers of prognosis in non-small cell lung cancer: a review and proposal for a multiphase approach to marker evaluation
Bibliographic record
Abstract
Characteristics of the tumour that affect and predict the survival outcome of patients with cancer are prognostic markers for cancer. In non-small cell lung carcinoma (NSCLC), stage is the main determinant of prognosis and the basis for deciding options for treatment. Patients with early-stage tumour are treated by complete surgical resection, which is curative in 40-70% of patients. That there are other factors important in determining the biology of these tumours, especially genes that have a role in metastasis, is indicated. Such factors could potentially be used to further classify patients into groups according to substages that may be treated differently. During the past decade, a large number of proteins that are putatively important in carcinogenesis and cancer biology have been studied for their prognostic value in NSCLC, but none of them have been proved to be sufficiently useful in clinical diagnosis. Several markers (epidermal growth factor receptor, human epidermal growth factor receptor 2, Ki-67, p53 and Bcl-2) have been studied exhaustively. Ki-67, p53 and Bcl-2 are suggested to be important but weak prognostic markers, by meta-analyses of the results. Cyclin E, vascular endothelial growth factor A, p16(INK4A), p27(kip1) and beta-catenin are promising candidates, but require further study in large randomised clinical trial samples by using standardised assays and scoring systems. Some issues and inconsistencies in the reported studies to date are highlighted and discussed. A guideline for a multi-phase approach for conducting future studies on prognostic immunohistochemistry markers is proposed here.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".