Evaluating the role for renal biopsy in T1 and T2 renal masses: A single-centre study
Bibliographic record
Abstract
INTRODUCTION: Once used primarily in the identification of renal metastasis and lymphomas, various urological bodies are now adopting an expanded role for the renal biopsy. We sought to evaluate the role of the renal biopsy in a Canadian context, focusing on associated adverse events, radiographic burden, and diagnostic accuracy. METHODS: This retrospective review incorporated all patients undergoing ultrasound (US)/computed tomography (CT)-guided biopsies for T1 and T2 renal masses. There were no age or lesion size limitations. The primary outcome of interest was the correlation between initial biopsy and final surgical pathology. A binomial logistic regression analysis was conducted to determine any confounding factors. Secondary outcomes included the accuracy of tumour cell typing, grading, the safety profile, and radiographic burden associated with these patients. RESULTS: A total of 148 patients satisfied inclusion criteria for this study. Mean age and lesions size at detection were 60.9 years (±12.4) and 3.6 cm (±2.0), respectively. Most renal masses were identified with US (52.7%) or CT (44.6%). Three patients (2.0%) experienced adverse events of note. Eighty-six patients (58.1%) proceeded to radical/partial nephrectomy. Our biopsies held a diagnostic accuracy of 90.7% (sensitivity 96.2%, specificity 87.5%, positive predictive value 98.7%, negative predictive value 70.0%, kappa 0.752, p<0.0005). Binomial logistic regression revealed that age, lesion size, number of radiographic tests, time to biopsy, and modality of biopsy (US/CT) had no influence on the diagnostic accuracy of biopsies. CONCLUSIONS: Renal biopsies are safe, feasible, and diagnostic. Their role should be expanded in the routine evaluation of T1 and T2 renal masses.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".