Cystic Renal Oncocytoma and Tubulocystic Renal Cell Carcinoma
Bibliographic record
Abstract
Renal oncocytoma (RO) may present with a tubulocystic growth in 3% to 7% of cases, and in such cases its morphology may significantly overlap with tubulocystic renal cell carcinoma (TCRCC). We compared the morphologic and immunohistochemical characteristics of these tumors, aiming to clarify the differential diagnostic criteria, which facilitate the discrimination of RO from TCRCC. Twenty-four cystic ROs and 15 TCRCCs were selected and analyzed for: architectural growth patterns, stromal features, cytomorphology, ISUP nucleolar grade, necrosis, and mitotic activity. Immunohistochemical panel included various cytokeratins (AE1-AE3, OSCAR, CAM5.2, CK7), vimentin, CD10, CD117, AMACR, CA-IX, antimitochondrial antigen (MIA), EMA, and Ki-67. The presence of at least focal solid growth and islands of tumor cells interspersed with loose stroma, lower ISUP nucleolar grade, absence of necrosis, and absence of mitotic figures were strongly suggestive of a cystic RO. In contrast, the absence of solid and island growth patterns and presence of more compact, fibrous stroma, accompanied by higher ISUP nucleolar grade, focal necrosis, and mitotic figures were all associated with TCRCC. TCRCC marked more frequently for vimentin, CD10, AMACR, and CK7 and had a higher proliferative index by Ki-67 (>15%). CD117 was negative in 14/15 cases. One case was weakly CD117 reactive with cytoplasmic positivity. All cystic RO cases were strongly positive for CD117. The remaining markers (AE1-AE3, CAM5.2, OSCAR, CA-IX, MIA, EMA) were of limited utility. Presence of tumor cell islands and solid growth areas and the type of stroma may be major morphologic criteria in differentiating cystic RO from TCRCC. In difficult cases, or when a limited tissue precludes full morphologic assessment, immunohistochemical pattern of vimentin, CD10, CD117, AMACR, CK7, and Ki-67 could help in establishing the correct diagnosis.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".