Acceptance of emerging renal oncocytic neoplasms: a survey of urologic pathologists
Bibliographic record
Abstract
Oncocytic renal neoplasms are a major source of diagnostic challenge in genitourinary pathology; however, they are typically nonaggressive in general, raising the question of whether distinguishing different subtypes, including emerging entities, is necessary. Emerging entities recently described include eosinophilic solid and cystic renal cell carcinoma (ESC RCC), low-grade oncocytic tumor (LOT), eosinophilic vacuolated tumor (EVT), and papillary renal neoplasm with reverse polarity (PRNRP). A survey was shared among 65 urologic pathologists using SurveyMonkey.com (Survey Monkey, Santa Clara, CA, USA). De-identified and anonymized respondent data were analyzed. Sixty-three participants completed the survey and contributed to the study. Participants were from Asia (n = 21; 35%), North America (n = 31; 52%), Europe (n = 6; 10%), and Australia (n = 2; 3%). Half encounter oncocytic renal neoplasms that are difficult to classify monthly or more frequently. Most (70%) indicated that there is enough evidence to consider ESC RCC as a distinct entity now, whereas there was less certainty for LOT (27%), EVT (29%), and PRNRP (37%). However, when combining the responses for sufficient evidence currently and likely in the future, LOT and EVT yielded > 70% and > 60% for PRNRP. Most (60%) would not render an outright diagnosis of oncocytoma on needle core biopsy. There was a dichotomy in the routine use of immunohistochemistry (IHC) in the evaluation of oncocytoma (yes = 52%; no = 48%). The most utilized IHC markers included keratin 7 and 20, KIT, AMACR, PAX8, CA9, melan A, succinate dehydrogenase (SDH)B, and fumarate hydratase (FH). Genetic techniques used included TSC1/TSC2/MTOR (67%) or TFE3 (74%) genes and pathways; however, the majority reported using these very rarely. Only 40% have encountered low-grade oncocytic renal neoplasms that are deficient for FH. Increasing experience with the spectrum of oncocytic renal neoplasms will likely yield further insights into the most appropriate work-up, classification, and clinical management for these entities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".