Solitary solid renal mass: can we predict malignancy?
Bibliographic record
Abstract
UNLABELLED: What's known on the subject? and What does the study add? It is known that the majority (80%) of solid renal masses are malignant. Most of the literature suggests that smaller tumour size is associated with a higher incidence of benign disease. We have confirmed that decreased tumour size is associated with benign disease, particularly for lesions <2 cm. Once radiologically apparent angiomyolipomas are excluded, the incidence of benign disease with small renal masses is lower than reported in the literature (11%). Furthermore, we have shown that small renal masses in females have a higher association with benign disease. Patient age and tumour location were not predictive of benign histology. We have also stratified our risks not only for benign disease but also for clinically indolent renal cancers to help physicians counsel patients with regard to managing these solid renal masses. OBJECTIVE: • To determine the clinical predictors of benign disease in patients with solitary solid renal masses. PATIENTS AND METHODS: • Pathology reports of patients who underwent radical or partial nephrectomy at two hospitals from 1998 to 2008 were reviewed. • Only patients with solitary solid unilateral renal masses were included. • Predictors of malignancy risk were assessed with univariate and multivariate logistic regression analysis. RESULTS: • A total of 592 patients with a mean (sd) age of 60 (13) years were included, 38% of whom were women. Radical and partial nephrectomy was performed in 66% and 34% of patients, respectively. • Renal masses were equally distributed on the right and left sides (49% vs 51%, P= 0.84). Masses were more commonly located in the upper and lower poles than in the mid pole (40.8% vs 38.7% vs 20.5%, respectively). • The mean tumour size was larger in patients who underwent radical compared with partial nephrectomy (6.8 cm vs 2.9 cm, P < 0.001). The rate of benign disease in our overall population was 9.5%. • On univariate and multivariate analysis, only a renal mass size <2 cm and female gender were predictive of benign disease. On further analysis the magnitude of this effect was found to be additive. CONCLUSIONS: • Renal masses <2 cm and female gender were associated with a higher probability of benign disease. • Patient age and tumour location were not predictive of benign disease.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".