Immunohistochemical panel for differentiating renal cell carcinoma with clear and papillary features
Bibliographic record
Abstract
OBJECTIVES: Renal cell carcinoma (RCC) in which clear cells with papillary architecture are present is a difficult diagnostic challenge. Clear cell RCC, rarely has papillary architecture. Papillary RCC rarely contains clear cells. However, two recently described types; clear cell papillary and Xp11 translocation RCC characteristically feature both papillary and clear cells. Accurate diagnosis has both prognostic and therapeutic implications. This study aims to highlight the helpful features of each of these entities to enable reproducible classification. METHODS: Sixty RCC cases with clear cells and papillary architecture were selected and classified according to The International Society of Urological Pathology (ISUP) Vancouver Classification of Renal Neoplasia and graded according to The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma then stained for CK7, carbonic anhydrase IX (CA IX), α-methylacyl-CoA-racemase (AMACR) and TFE-3. RESULTS: The characteristic immunoprofile of Clear RCC is CK7-, AMACR-, CA IX+ and TFE3-, papillary RCC is CK7+, AMACR+, CAIX- and TFE3-, while for clear cell papillary RCC it is CK7+, AMACR-, CAIX+ and TFE3- and lastly Xp11 translocation RCC is CK7-, AMACR+, CAIX- and TFE3+. CONCLUSIONS: Staining for CA IX, CK7, AMACR and TFE3 comprises a concise panel for distinguishing RCC with papillary and clear pattern.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".