Growth kinetics of small renal masses: A prospective analysis from the Renal Cell Carcinoma Consortium of Canada
Bibliographic record
Abstract
INTRODUCTION: Most small renal masses (SRMs) are diagnosed incidentally and have a low malignant potential. As more elderly patients and infirm patients are diagnosed with SRMs, there is an increased interest in active surveillance (AS) with delayed intervention. Patient and tumour characteristics relating to aggressive disease have not been well-studied. The objective was to determine predictors of growth of SRMs treated with AS. METHODS: A multicentre prospective phase 2 clinical trial was conducted on 207 SRMs in 169 patients in 8 institutions in Canada from 2004 to 2009; in these patients treatment was delayed until disease progression. Patient and tumour characteristics were evaluated to determine predictors of growth of SRMs by measuring rates of change in growth (on imaging) over time. All patients underwent AS for presumed renal cell carcinoma (RCC) based on diagnostic imaging. We used the following factors to develop a predictive model of tumour growth with binary recursive partitioning analysis: patient characteristics (age, symptoms at diagnosis) and tumour characteristics (consistency [solid vs. cystic] and maximum diameter at diagnosis. RESULTS: With a median follow-up of 603 days, 169 patients (with 207 SRMs) were followed prospectively. Age, symptoms at diagnosis, tumour consistency and maximum diameter of the renal mass were not predictors of growth. This cohort was limited by lack of availability of patient and tumour characteristics, such as sex, degree of endophytic component and tumour location. CONCLUSION: Slow growth rates and the low malignant potential of SRMs have led to AS as a treatment option in the elderly and infirm population. In a large prospective cohort, we have shown that age, symptoms, tumour consistency and maximum diameter of the mass at diagnosis are not predictors of growth of T1a lesions. More knowledge on predictors of growth of SRMs is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".