Active Surveillance of Biopsy-Confirmed Oncocytic Renal Tumors: Growth Dynamics and Impact on Renal Function
Bibliographic record
Abstract
PURPOSE: Oncocytic neoplasms account for up to 15% of small renal masses. Active surveillance (AS) is increasingly adopted for these lesions; however, knowledge gaps remain regarding their growth kinetics, concordance with renal tumor biopsy, and impact on renal function. This study reviews these factors in the largest cohort of biopsy-confirmed oncocytic neoplasms managed with AS. MATERIALS AND METHODS: This single-center, retrospective study included patients with biopsy-confirmed oncocytoma or chromophobe renal cell carcinoma (chRCC) on AS (2003-2021). Tumor growth rates and change in renal function (estimated glomerular filtration rate) were analyzed using linear mixed models, while development of chronic kidney disease was assessed using Cox proportional hazards model. Biopsy-to-surgical pathology concordance and triggers for intervention were examined. RESULTS: = .6). There were no events of metastases or kidney cancer-related deaths over a median follow-up of 5.7 years (IQR: 3.0, 8.7). CONCLUSIONS: Oncocytic lesions on AS demonstrate slow growth, low intervention rate, preservation of renal function, and excellent disease-specific survival. Although biopsy limitations exist, histology-guided AS provides effective 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".