Is there a role for anterior zone sampling as part of saturation trans-rectal ultrasound guided prostate biopsy?
Bibliographic record
Abstract
BACKGROUND: The prostatic anterior zone (AZ) is not targeted routinely by TRUS guided prostate biopsy (TRUS-Pbx). MRI is an accurate diagnostic tool for AZ tumors, but is often unavailable due to cost or system restrictions. We examined the diagnostic yield of office based AZ TRUS-Pbx. METHODS: 127 men at risk for AZ tumors were studied: Patients with elevated PSA and previous extended negative TRUS-Pbx (group 1, n = 78) and actively surveyed low risk prostate cancer patients (group 2, n = 49). None of the participants had a previous AZ biopsy. Biopsy template included suspicious ultrasonic areas, 16 peripheral zone (PZ), 4 transitional zone (TZ) and 6 AZ cores. All biopsies were performed by a single urologist under local peri-prostatic anaesthetic, using the B-K Medical US System, an end-firing probe 4-12 MHZ and 18 ga/25 cm needle. All samples were reviewed by a single specialized uro-pathologist. Multivariate analysis was used to detect predictors for AZ tumors accounting for age, PSA, PSA density, prostate volume, BMI, and number of previous biopsies. RESULTS: Median PSA was 10.4 (group 1) and 7.3 (group 2). Age (63.9, 64.5), number of previous biopsies (1.5) and cores (17.8, 21.3) and prostate volume (56.4 cc, 51 cc) were similar for both groups. The overall diagnostic yield was 34.6% (group 1) and 85.7% (group 2). AZ cancers were detected in 21.8% (group 1) and 34.7% (group 2) but were rarely the only zone involved (1.3% and 4.1% respectively). Gleason ≥ 7 AZ cancers were often accompanied by equal grade PZ tumors. In multivariate analysis only prostate volume predicted for AZ tumors. Patients detected with AZ tumors had significantly smaller prostates (36.9 cc vs. 61.1 cc p < 0.001). Suspicious AZ ultrasonic findings were uncommon (6.3%). CONCLUSIONS: TRUS-Pbx AZ sampling rarely improves the diagnostic yield of extended PZ sampling in patients with elevated PSA and previous negative biopsies. In low risk prostate cancer patients who are followed by active surveillance, AZ sampling changes risk stratification in 6% but larger studies are needed to define the role of AZ sampling in this population and its correlation with prostatectomy final pathological specimens.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".