False-negative Prostate Needle Biopsies: Frequency, Histopathologic Features, and Follow-up
Bibliographic record
Abstract
Little is known about the frequency, histopathologic characteristics, and clinical consequences of false-negative prostate biopsies, that is, biopsies classified as benign but containing adenocarcinoma or atypical suspicious glands [atypical small acinar proliferations (ASAP)]. Objective of this study was to evaluate false-negative prostate biopsy in a prostate cancer screening setting. Prostate biopsy sets of 196 participants of a screening trial, which had been reported as "benign" at initial diagnosis, followed by a diagnosis of adenocarcinoma in a subsequent screening round were reviewed by 2 urologic pathologists. Adenocarcinoma was identified in 19 biopsy cores corresponding to 16 (8.2%) patients and ASAP in 24 cores, corresponding to 19 patients (9.7%). All missed prostate cancers were Gleason score 6 (3+3). After correction for patient selection, the overall false-negative biopsy rate was estimated to be 2.4%; 1.1% for prostate cancer; and 1.3% for ASAP. Clinicopathologic features at the time of initial biopsy and of subsequent prostate cancer diagnosis did not differ between patients with a false-negative or true benign biopsy. Relatively low number of atypical glands (<10 glands), intense intermingling with preexistent glands or lack of architectural disorganization were the most prominent risk factors for a false-negative diagnosis. Another potential pitfall was the presence of prostate cancer variants, as 1 adenocarcinoma was of foamy gland type and 3 of pseudohyperplastic type. Routine examination of at least 1 level of prostate biopsy sets at high magnification and awareness of histologic prostate cancer variants might reduce the risk of missing or misinterpreting a relevant lesion at prostate biopsy evaluation.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".