Handling and reporting of pelvic lymphadenectomy specimens in prostate and bladder cancer: a web‐based survey by the European Network of Uropathology
Bibliographic record
Abstract
AIMS: Pathological evaluation of lymphadenectomy specimens plays a pivotal role in accurate lymph node (LN) staging. Guidelines standardising the gross handling and reporting of pelvic LN dissection (PLND) in prostate (PCa) and bladder (BCa) cancer are currently lacking. This study aimed to establish current practice patterns of PLND evaluation among pathologists. METHODS AND RESULTS: A web-based survey was circulated to all members of the European Network of Uropathology (ENUP), comprising 29 questions focusing on the macroscopic handling, LN enumeration and reporting of PLND in PCa and BCa. Two hundred and eighty responses were received from pathologists throughout 23 countries. Only LNs palpable at grossing were submitted by 58%, while 39% routinely embedded the entire specimen. Average LN yield from PLND was ≥10 LNs in 56% and <10 LNs in 44%. Serial section(s) and immunohistochemistry were routinely performed on LN blocks by 42% and <1% of respondents, respectively. To designate a LN microscopically, 91% required a capsule/subcapsular sinus. In pN+ cases, 72% reported the size of the largest metastatic deposit and 94% reported extranodal extension. Isolated tumour cells were interpreted as pN1 by 77%. Deposits identified in fat without associated lymphoid tissue were reported as tumour deposits (pN0) by 36% and replaced LNs (pN+) by 27%. LNs identified in periprostatic fat were included in the PLND LN count by 69%. CONCLUSION: This study highlights variations in practice with respect to the gross sampling and microscopic evaluation of PLND in urological malignancies. A consensus protocol may provide a framework for more consistent and standardised reporting of PLND specimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".