Immunohistochemical biomarker scoring in gastroesophageal cancers: Can computers help us?
Bibliographic record
Abstract
The increasing complexity of cancer diagnostics and treatment selection has placed a growing burden on pathologists, particularly in the evaluation of immunohistochemical (IHC) biomarkers. In gastroesophageal cancers (GEC), both adenocarcinoma and squamous cell carcinoma subtypes, multiple prognostic and predictive biomarkers must be assessed to guide therapy. These evaluations require meticulous scoring, are time-consuming, and suffer from inter- and intra-observer variability. Given the worldwide shortage of pathologists, artificial intelligence (AI)-based tools have emerged as a potential solution to enhance efficiency and accuracy in biomarker scoring. This review aims to answer the question captured in its title: can AI help us in IHC biomarker scoring in GEC, and if so, how? A search of PubMed and Google Scholar was conducted to identify relevant studies. The analysis reveals that AI has demonstrated promise in improving reproducibility and reducing pathologist workload for biomarkers such as PD-L1 and HER2, although its applications in GEC remain limited compared to other cancer types. In parallel, predictive computational approaches are emerging that could revolutionize biomarker scoring altogether. By alleviating the burdens of complex scoring systems and costly additional assays, AI could have the potential to significantly enhance pathology practice in GEC biomarker evaluation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".