The natural history of incidentally detected small renal masses
Bibliographic record
Abstract
BACKGROUND: The incidence of renal cell carcinoma (RCC) is increasing, largely due to the widespread use of cross-sectional imaging. Most renal tumors are detected incidentally as small, asymptomatic masses. To study their natural history, the authors prospectively followed a series of patients with this type of lesion who were unsuited for or refused surgery. METHODS: Twenty-nine patients with 32 masses that measured < 4 cm in greatest dimension (25 solid masses and 7 complex cystic masses) were studied. The primary outcome was tumor size, which was calculated as volume over time. All patients were followed with serial abdominal imaging, and each mass had at least three follow-up measurements. The median follow-up was 27.9 months (range, 5.3-143.0 months). RESULTS: Overall, the average growth rate did not differ statistically from zero growth (P = 0.09; 95% confidence interval, - 0.005-0.2 cm per year) and was not associated with either initial size (P = 0.28) or mass type (P = 0.41). Seven masses (22%) reached 4 cm in greatest dimension after 12-85 months of follow-up. Eight masses (25%) doubled their volumes within 12 months. Overall, 11 masses (34%) fulfilled 1 of these 2 criteria of rapid growth. Nine tumors were removed surgically after an average of 3.1 years of follow-up because it was believed that they were growing fast. No patient had disease progression. CONCLUSIONS: Approximately one-third of small renal masses that are presumed RCCs grow if they are managed conservatively and are followed with serial imaging. The growth rate is slow or undetectable in the majority of patients. These observations raise the possibility of a period of initial observation in selected patients, particularly the elderly or infirm.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".