Renal oncocytoma revisited: a clinicopathological study of 109 cases with emphasis on problematic diagnostic features
Bibliographic record
Abstract
AIM: To evaluate problematic diagnostic features in renal oncocytoma. METHODS AND RESULTS: One hundred and nine cases of oncocytoma were reviewed and the problematic gross and microscopic features recorded. Multifocal and bilateral neoplasms were found in 12 (11%) and five (4.6%) cases, respectively. Haemorrhage was seen grossly in 30 (27.5%) neoplasms and a central scar was identified in 35 (32.1%). On microscopy, perinephric fat extension was present in 17 (15.6%) neoplasms and vascular extension was identified in four (3.7%) oncocytomas. Rare mitoses and focal coagulative necrosis were identified in two (1.8%) cases each. Focal clear cell changes were found in 16 (14.7%) oncocytomas, typically within hyalinized areas. Limited foci with chromophobe-like histology (not exceeding 5% of the neoplasm) were found in 13 (11.9%) oncocytomas. In 12 (11%) oncocytomas, rare papillary formations were noted in the lumina of microcysts. Significant nuclear atypia, oncoblasts and entrapped tubules were identified in 27 (24.8%), 41 (37.6%) and 40 (36.7%) neoplasms, respectively. After a median follow-up of 52 months (range 1-113 months), there was no disease recurrence, progression or death attributed to oncocytoma. CONCLUSIONS: The recognition of the spectrum of morphological changes observed in renal oncocytoma should help pathologists establish a diagnosis of oncocytoma in problematic cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".