Active Surveillance for Renal Neoplasms with Oncocytic Features is Safe
Bibliographic record
Abstract
PURPOSE: Oncocytomas are benign tumors often diagnosed incidentally on imaging. Small case series have suggested that the growth kinetics of oncocytomas are similar to those of malignant renal tumors. Biopsy material may be insufficient to exclude a diagnosis of chromophobe renal cell carcinoma. We evaluated and compared the growth rates of oncocytoma and chromophobe renal cell carcinoma to improve our understanding of their natural history. MATERIALS AND METHODS: This was a single center, retrospective study of patients diagnosed with lesions suggestive of oncocytoma or chromophobe renal cell carcinoma between 2003 and 2014. The growth rates were estimated using a mixed effect linear model. Patient and lesion characteristics were tested using a similar model for association with growth rate. RESULTS: Of the 95 lesions (oncocytoma 81, chromophobe renal cell carcinoma 14) included in the analysis 98% were diagnosed on biopsy. The annual growth rate was 0.14 cm and 0.38 cm for oncocytoma (median followup 34 months) and chromophobe renal cell carcinoma (median followup 25 months), respectively (p=0.5). Baseline lesion size was significantly associated with growth (p <0.001). The majority of oncocytomas (74%) and chromophobe renal cell carcinomas (67%) followed up to the 3-year mark had grown. Of these, 8 underwent surgery (6 in the chromophobe renal cell carcinoma group). The initial diagnosis was confirmed in all. Overall 5 patients died, all of nonrenal related causes. CONCLUSIONS: Although the majority of oncocytic renal neoplasms will grow with time, surveillance appears to remain safe. Patients opting for this strategy should be made aware that a diagnosis of oncocytoma following biopsy is associated with some degree of uncertainty due to the difficulty of differentiating them from other oncocytic renal neoplasms.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".