Carbonic anhydrase IX (CA9) expression in multiple renal epithelial tumour subtypes
Bibliographic record
Abstract
AIMS: Renal epithelial neoplasms (RENs) can be difficult to subclassify, owing to overlapping morphological features. Carbonic anhydrase 9 (CA9) is a common biomarker for clear cell renal cell carcinoma (CCRCC); however, the sensitivity and specificity across REN subtypes are less clear. The aim of this study was to investigate CA9 expression in RENs, especially those in the differential diagnosis with CCRCC and less common entities, to determine its reliability as a diagnostic biomarker. METHODS AND RESULTS: CA9 immunostaining was performed on 262 RENs, including 119 CCRCCs and 143 non-CCRCC. Immunostaining was evaluated as negative (0%), rare (1+, 1-10%), focal (2+, 11-50%), or diffuse (3+, >50%). CCRCCs were 3+ CA9-positive in 93% of cases; 4% were CA9-negative. Sixty-seven percent of papillary renal cell carcinomas (RCCs) were 1+/2+ CA9-positive, whereas 33% were CA9-negative. Chromophobe RCCs were nearly always CA9-negative (93%), with 7% showing rare cell reactivity. Clear cell tubulopapillary RCCs (CCTPRCCs) were consistently 3+ CA9-positive, but with a cup-like staining pattern. Fifty-three percent of Xp11.2 RCCs were CA9-negative; however, 6% were 3+ CA9-positive and 12% were 2+ CA9-positive. Two of eight fumarate hydratase-deficient RCCs were 3+ CA9-positive. A small subset of the remaining RCCs showed rare to focal CA9 expression. All oncocytomas and eosinophilic solid and cystic RCCs were CA9-negative. CONCLUSIONS: Overall, diffuse CA9 expression was identified in nearly all CCRCCs and in all CCTPRCCs (high sensitivity); however, CA9 was not entirely specific. At least focal CA9 expression can been seen in a subset of many RCCs, and such findings should be taken into consideration with other morphological, immunophenotypic and clinical findings.
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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 | 0.001 | 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) | 0.001 | 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".