Current practices in prostate pathology reporting: results from a survey of genitourinary and general pathologists
Bibliographic record
Abstract
AIMS: Standardizing pathology reporting protocols through peer consensus review is critical for the best quality of care metrics. Reporting heterogeneity due to discrepancies among professional societies and practice patterns may lead to heterogeneous management and treatment approaches. This issue prompted a multi-institutional survey of pathologists to address potential similarities or differences in trends and practice patterns in prostate pathology reporting worldwide. METHODS AND RESULTS: A REDCap survey was distributed among 175 pathologists worldwide, recruited through invitations and social media. The response rate among invited pathologists was 83%. The practice locations were as follows: North America (USA, Canada, and Mexico, 62%), Europe (17%), Australia/New Zealand (3%), Central/South America (2%), Asia (13%), and Africa (2%). Most pathologists practiced for <5 years (28%). A genitourinary (GU) pathology fellowship was completed by 37%, 58% practiced in a subspecialized setting, and 43% in academia. Reporting includes (63%) or subtracts (37%) intervening benign tissue. Both Gleason score and Grade Groups (GG)s were reported by 96% of responders, whereas 94% report percent pattern 4 (%4). Aggregate grading and volume estimation in undesignated cores with different grades in the same jar are reported by 73% and 54% for systematic biopsies, and 83% and 62% for targeted biopsies, respectively. Cribriform morphology was reported by 81%. For presumed intraductal carcinoma (IDC), 89% use basal cell markers when isolated (iIDC), 82% with GG1 cancer, and 37% with ≥GG2. iIDC or IDC associated with GG1 or with ≥GG2 was not graded by 90%, 78%, and 70%, respectively. In radical prostatectomies, 90% report %4, but only 53% report it if the overall grade is ≥7. A tumour with Gleason 3 + 3 = 6 and <5% pattern 4 was graded as GG2 by 64%. A <5% cutoff for defining tertiary pattern was used by 74%, and 80% report >5% pattern 4 or 5 as a secondary pattern. Grading was assigned based on the dominant nodule by 59%. Finally, reporting practices were significantly associated with demographic characteristics. CONCLUSIONS: Although most issues are agreed upon, significant discordance is identified among societies and pathologists in different practice settings. We hope this survey will serve as the basis for future studies and new collaborative approaches to more standardized reporting practices.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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 | 0.001 |
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".