Diagnosis of “Poorly Formed Glands” Gleason Pattern 4 Prostatic Adenocarcinoma on Needle Biopsy
Bibliographic record
Abstract
Accurate recognition of Gleason pattern (GP) 4 prostate carcinoma (PCa) on needle biopsy is critical for patient management and prognostication. "Poorly formed glands" are the most common GP4 subpattern. We studied the diagnostic reproducibility and the quantitative threshold of grading GP4 "poorly formed glands" and the criteria to distinguish them from tangentially sectioned GP3 glands. Seventeen urologic pathologists were first queried for the definition of "poorly formed glands" using cases representing a spectrum of PCa glandular differentiation. Cancer glands with no or rare lumens, elongated compressed glands, and elongated nests were considered "poorly formed glands" by consensus. Participants then graded a second set of 23 PCa cases that potentially contained "poorly formed glands" with a fair interobserver agreement (κ = 0.34). The consensus diagnoses, defined as agreement by > 70% participants, were then correlated with the quantitative (≤ 5, 6 to 10, >10) and topographic features of poorly formed glands (clustered, immediately adjacent to, and intermixed with other well-formed PCa glands) in each case. Poorly formed glands immediately adjacent to other well-formed glands regardless of their number and small foci of ≤ 5 poorly formed glands regardless of their location were not graded as GP4. In contrast, large foci of >10 poorly formed glands that were not immediately adjacent to well-formed glands were graded as GP4. Grading "poorly formed glands" is challenging. Some morphologic features are, however, reproducible for and against a GP4 diagnosis. This study represents an important step in standardization of grading of "poorly formed glands" based on quantitative and topographic morphologic features.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".