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 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 | 0.001 | 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 | 0.001 |
| 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".