Oncocytic papillary renal cell carcinoma with solid architecture: Mimic of renal oncocytoma
Bibliographic record
Abstract
Papillary renal cell carcinoma (PRCC) can display extensive areas of solid and non-papillary architecture and extensive areas with oncocytic cytoplasm. Eleven oncocytic renal cell neoplasms (ORCN) with histopathological features posing a diagnostic problem between renal cell carcinoma (RCC) with oncocytic features and renal oncocytoma (RO) were identified. The neoplasms were well circumscribed or encapsulated tumors with solid and diffuse growth pattern. Very occasional papillae were seen in four and tumoral necrosis in two of 11. Six ORCN displayed a CD117+/progesterone receptor (PR)+ immunophenotype (feature shared by RO) and five tumors displayed a CD117-/PR- immunophenotype (feature shared by RCC). The CD117-/PR- ORCN also displayed alpha-methylacyl-coenzyme A racemase and RCC antigen reactivity as well as varying reactivity for cytokeratin 7, vimentin and CD10 (features of oncocytic PRCC). These five cases had tumor sizes ranging from 1 to 6 cm. Two patients in the latter group developed progression of the disease with metastases. In conclusion, oncocytic PRCC with solid architecture is a rare type of RCC. The carcinoma often poses differential diagnostic problems with RO and has similar immunohistochemical properties to the common type of PRCC. Cytogenetic and molecular studies have not been performed yet for this variant of RCC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".