Dilemmas in diagnosis and natural history of renal oncocytoma and implications for management
Bibliographic record
Abstract
INTRODUCTION: Oncocytomas have traditionally been treated with surgical excision; however, their excellent long-term prognosis has popularized conservative and minimally invasive ablative techniques. We evaluated the evolving management and natural history of renal oncocytomas and investigated the relationship between radiological and histopathological diagnosis. METHODS: We performed a 17-year retrospective cohort study on all patients with a confirmed histopathological diagnosis of renal oncocytoma. The primary outcome variables were long-term outcomes, coexistence with renal cell carcinoma, and development of metastatic disease. RESULTS: A total of 38 oncocytomas were reported in 36 patients. Of the 36 patients, 29 (81%) were diagnosed incidentally. Oncocytoma was considered in the differential diagnosis in 4 oncocytomas (10.5%). In total, 34 patients underwent early surgical intervention; of these, 27 (79.4%) underwent radical nephrectomy and 7 underwent partial nephrectomy (20.6%). Four patients (11.1%) were managed conservatively with surveillance. No patients developed recurrence or metastatic disease after a median follow-up of 84 months (range: 4-178). CONCLUSIONS: The diagnostic accuracy for imaging modalities in renal oncocytoma is poor. Surveillance or minimally invasive ablative techniques are appropriate in selected patients with biopsy-proven oncocytoma that are not increasing in size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".