Variability in Diagnostic Opinion Among Pathologists for Single Small Atypical Foci in Prostate Biopsies
Bibliographic record
Abstract
Pathologists are increasingly exposed to prostate biopsies with small atypical foci, requiring differentiation between adenocarcinoma, atypical small acinar proliferation suspicious for malignancy, and a benign diagnosis. We studied the level of agreement for such atypical foci among experts in urologic pathology and all-round reference pathologists of the European Randomized Screening study of Prostate Cancer (ERSPC). For this purpose, we retrieved 20 prostate biopsies with small (most <1 mm) atypical foci. Hematoxylin and eosin-stained slides, including 10 immunostained slides were digitalized for virtual microscopy. The lesional area was not marked. Five experts and 7 ERSPC pathologists examined the cases. Multirater kappa statistics was applied to determine agreement and significant differences between experts and ERSPC pathologists. The kappa value of experts (0.39; confidence interval, 0.29-0.49) was significantly higher than that of ERSPC pathologists (0.21; confidence interval, 0.14-0.27). Full (100%) agreement was reached by the 5 experts for 7 of 20 biopsies. Experts and ERSPC pathologists rendered diagnoses ranging from benign to adenocarcinoma on the same biopsy in 5 and 9 biopsies, respectively. Most of these lesions comprised between 2 and 5 atypical glands. The experts diagnosed adenocarcinoma (49%) more often than the ERSPC pathologists (32%) (P<0.001). As agreement was particularly poor for foci comprising <6 glands, we would encourage pathologists to obtain intercollegial consultation of a specialized pathologist for these lesions before a carcinoma diagnosis, whereas clinicians may consider to perform staging biopsies before engaging on deferred or definite therapy.
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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 | 0.054 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".