An interobserver reproducibility study on invasiveness of bladder cancer using virtual microscopy and heatmaps
Bibliographic record
Abstract
AIMS: The distinction between non-invasive (pTa) and invasive (pT1) non-muscle invasive bladder cancer (NMIBC) is subject to considerable interobserver variation. We aimed to generate a teaching set of images based on the diagnostic opinions of a panel of expert genitourinary pathologists. METHODS AND RESULTS: Twenty-five transurethral resection specimens initially reported as pT1 NMIBC from two university hospitals were selected on the basis of potential uncertainty of stromal invasion. Digitized slides were reviewed independently by a panel of eight genitourinary pathologists, who annotated any invasive area if present. Annotations were reviewed by the lead panel, and heatmaps of annotated areas were constructed. Reasons for discrepancies were analysed, and kappa scores were calculated to determine agreement among the eight panellists. Full agreement by the eight panellists was obtained in 11 of 25 cases (44%), with a multi-rater (Fleiss) kappa of 0.47 (P < 0.0001). After joint review of the seven discordant (agreement <75% of panellists) cases, consensus was obtained for six cases, and a teaching set of images was generated. CONCLUSIONS: Interobserver agreement among the panellists in the selected cases was moderate, but consensus could be reached in almost all cases. Heatmaps proved to be instrumental in generating a teaching set of images for standardization of histological criteria for NMIBC invasion.
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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 | 0.057 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".