Interobserver Reproducibility of Pelvicalyceal Invasion in Renal Cell Carcinoma Nephrectomies Among Genitourinary Pathologists
Bibliographic record
Abstract
Pelvicalyceal invasion (PCI) is a relatively novel pT3a staging parameter for renal cell carcinoma (RCC) nephrectomies. While interobserver reproducibility staging studies of sinus/vascular invasion in RCC exist, a similar evaluation for PCI has not been performed. Moreover, in our experience, there is also diagnostic variability in how pathologists interpret PCI. Herein, we explore interobserver reproducibility among genitourinary (GU) pathologists. Twenty hematoxylin and eosin-stained digitized slides from RCCs (all grossly approaching the renal pelvis) were distributed to 31 GU pathologists to classify each as PCI or not PCI based on their respective clinical practices; slides with concomitant sinus/fat/vascular invasion were excluded. Slides were then evaluated for the following 4 morphologic features: tumor abutting renal pelvis, tumor pushing/indenting into the renal pelvis, polypoid configuration of tumor into the renal pelvis, and tumor eroding through renal pelvic urothelium. Interobserver reproducibility was assessed, and the morphologic features were correlated with PCI. Relationships between pathologists' interpretations, morphologic features, and PCI were evaluated using hierarchical clustering. Although the diagnosis of PCI was relatively uniform with a majority agreement (>67%) reached in 16/20 slides, overall interobserver reproducibility was only moderate (kappa=0.601). While all 4 morphologic features were sensitive for PCI, polypoid configuration of the tumor into the renal pelvis and the tumor eroding through the renal pelvic urothelium were most specific (90%, 100%, respectively). Although we show general consensus among genitourinary pathologists on PCI assessment, clarifying the diagnostic guidelines with specific criteria should be included in pathologic staging systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".