Tissue-Based Immunohistochemical Markers for Diagnosis and Classification of Renal Cell Carcinoma
Bibliographic record
Abstract
The development and description of renal cell carcinoma (RCC) subtypes has led to an increase in demand for tissue biomarkers. This has implications not only in informing diagnosis, but also in guiding treatment selection and in prognostication. Although historically, many immunohistochemical (IHC) stains have been widely characterized for RCC subtypes, challenges may arise in interpreting these results. These may include variations in tumor classification, specimen collection and processing, and IHC techniques. In light of the reclassification of RCC subtypes in 2016, there remains a requirement for a comprehensive outline of tissue biomarkers that may be used to differentiate between RCC subtypes and distinguish these from other non-renal neoplasms. In this review, concise summaries of the commonest RCC subtypes, including clear cell, papillary, and chromophobe RCC, have been provided. Important differences have been highlighted between chromophobe RCC and renal oncocytomas. An overview of the current landscape of tissue biomarkers in other RCC subtypes has also been explored, revealing the variable staining results reported for some markers, whilst highlighting the essential markers for diagnosis in other subtypes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.002 | 0.002 |
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".