Diagnostic criteria for oncocytic renal neoplasms: a survey of urologic pathologists
Bibliographic record
Abstract
Renal oncocytoma and chromophobe renal cell carcinoma have been long recognized as distinct tumors; however, it remains unknown if uniform diagnostic criteria are used to distinguish these tumor types in practice. A survey was distributed to urologic pathologists regarding oncocytic tumors. Responses were received from 17 of 26 invitees. Histologically, more than 1 mitotic figure was regarded as most worrisome (n=10) or incompatible (n=6) with oncocytoma diagnosis. Interpretation of focal nuclear wrinkling, focal perinuclear clearing, and multinucleation depended on extent and did not necessarily exclude oncocytoma if minor. Staining techniques most commonly used included the following: cytokeratin 7 (94%), KIT (71%), vimentin (65%), colloidal iron (59%), CD10 (53%), and AMACR (41%). Rare cytokeratin 7-positive cells (≤5%) were regarded as most supportive of oncocytoma, although an extent excluding oncocytoma was not universal. Multiple chromosomal losses were most strongly supportive for chromophobe renal cell carcinoma diagnosis (65%). Less certainty was reported for chromosomal gain or a single loss. For tumors with mixed or inconclusive features, many participants use an intermediate diagnostic category (82%) that does not label the tumor as unequivocally benign or malignant, typically "oncocytic neoplasm" or "tumor" with comment. The term "hybrid tumor" was used variably in several scenarios. A slight majority (65%) report outright diagnosis of oncocytoma in needle biopsies. The morphologic, immunohistochemical, and genetic characteristics that define oncocytic renal tumors remain incompletely understood. Further studies correlating genetics, behavior, and histology are needed to define which tumors truly warrant classification as carcinomas for patient counseling and follow-up strategies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".