Diagnosing oncocytic renal tumours on renal mass biopsy; pathological concordance and the impact of evolving classification
Bibliographic record
Abstract
AIMS: Renal tumours with oncocytic morphology are among the most difficult to classify at renal mass biopsy (RMB), and a number of emerging entities with low-grade oncocytic morphology have been recently described. This study aimed to evaluate pathological concordance between RMB and subsequent nephrectomy or repeat biopsy for oncocytic renal neoplasms and to identify pathological factors contributing to diagnostic discordance, including the impact of evolving tumour classification. METHODS AND RESULTS: We retrospectively reviewed 145 cases of oncocytic renal neoplasms diagnosed on RMB, including 114 with subsequent nephrectomy and 31 with repeat biopsy only. Overall concordance was 92.9% between RMB and nephrectomy and 96.7% between initial and repeat RMB. Concordance for oncocytoma at nephrectomy was lower (81.4%), likely reflecting selection bias, but was 100% in cases with repeat biopsy. Review of discordant cases (n = 9) revealed that 55% (5/9) were reclassified as emerging tumour entities, specifically low-grade oncocytic tumour (LOT) and eosinophilic vacuolated tumour (EVT). Additional discordant cases were due to heterogeneous tumour morphology in chromophobe renal cell carcinoma (ChRCC) and incomplete immunohistochemical work-up leading to misclassification of rarer renal cell carcinoma subtypes. CONCLUSIONS: Despite inherent diagnostic challenges, there was overall good concordance between RMB and nephrectomy or subsequent biopsy for the diagnosis of oncocytic tumours. Recognition of emerging tumour entities may reduce diagnostic uncertainty, improve classification in challenging cases, and further improve diagnostic concordance over time. Nonetheless, limitations of RMB, particularly related to tumour heterogeneity, highlight the importance of integrating pathological, clinical, and radiologic data to inform patient management.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".