Low interobserver agreement in cytology grading of mucinous pancreatic neoplasms
Bibliographic record
Abstract
BACKGROUND: Identifying high-grade features in patients with pancreatic mucinous neoplasms (MNs) is important for patient management. The reproducibility of MN cytology grading has been evaluated to a limited extent. In the current study, the authors evaluated interobserver variability in grading MNs and the identification of neoplastic mucin in endoscopic ultrasound-guided fine-needle aspiration specimens. METHODS: A 54-case grading set was created from histologically confirmed MNs (44 MNs) and nonmucinous lesions with abundant gastrointestinal contamination (10 nonmucinous lesions). Six observers received a tutorial, reviewed prescreened slides, and recorded: 1) a diagnosis according to a 6-tiered system (TS) (nondiagnostic, atypical [ATP], mucinous cyst low grade [MCLG], mucinous cyst high grade, suspicious for adenocarcinoma, and positive for adenocarcinoma); 2) the cyst fluid carcinoembryonic antigen diagnosis (CEADX); and 3) the presence of neoplastic musin. Interobserver agreement (IOA) was evaluated by calculation of kappa coefficients (Kappa). Diagnostic accuracy was not evaluated. RESULTS: The IOA was lowest for the 6-TS (Kappa, 0.13; P<.001). The CEADX was available for 18 cases (33%), including 6 of 24 MCLG cases (25%). CEADX modestly improved IOA for combined tiers of the 6-TS with ATP and MCLG as separate categories. The highest IOA was noted with a 3-TS (nondiagnostic, ATP/MCLG, and mucinous cyst high grade/suspicious for adenocarcinoma/positive for adenocarcinoma [Kappa, 0.28; P<.001]) and various 4-TS (Kappa, 0.22-0.23). IOA was found to be low for neoplastic mucin (Kappa = 0.15; P<.001). CONCLUSIONS: In a study using simulated cytology practice, observers demonstrated fair IOA for grading MNs and low IOA for identifying neoplastic mucin. Knowledge of the cyst fluid CEA level was found to modestly improve the IOA for low-grade lesions.
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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 | 0.042 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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, 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".