Accuracy of renal tumour biopsy for the diagnosis and subtyping of papillary renal cell carcinoma: analysis of paired biopsy and nephrectomy specimens with focus on discordant cases
Bibliographic record
Abstract
AIMS: Renal tumour biopsy (RTB) is increasingly recognised as a useful diagnostic tool in the management of small renal masses, particularly those that are incidentally found. Intratumoural heterogeneity with respect to morphology, grade and molecular features represents a frequently identified limitation to the use of RTB. While previous studies have evaluated pathological correlation between RTB and nephrectomy, no studies to date have focused specifically on the role of RTB for the diagnosis of papillary renal cell carcinoma (PRCC) and its further subclassification into clinically relevant subtypes. METHODS: This single-institution study evaluated 60 cases of PRCC for concordance between RTB and nephrectomy with respect to diagnosis, grading and subtyping (type 1/type 2). RESULTS: We observed 93% concordance (55 of 59 evaluable cases) between RTB and nephrectomy for the diagnosis of PRCC, although seven tumours (12%) were undergraded on RTB. Subtyping of PRCC on RTB was concordant with nephrectomy in 89% of cases reported as type 1 PRCC on RTB (31/35), but only 40% of cases reported as type 2 PRCC on RTB (4/10). Morphological misclassification of PRCC on RTB was most likely to occur in tumours showing a solid growth pattern. Discordant PRCC subtyping most often occurred in tumours with eosinophilia/oncocytic change. CONCLUSION: There was good concordance between RTB and nephrectomy for the primary diagnosis of PRCC. Although further subtyping of PRCC can aid therapeutic stratification, this can be challenging on RTB and tumours with overlapping or ambiguous features are best reported as PRCC not otherwise specified pending development of more robust methods to facilitate definitive subclassification.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".