Patient selection determines the prostate cancer yield of dynamic contrast‐enhanced magnetic resonance imaging‐guided transrectal biopsies in a closed 3‐Tesla scanner
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cancer yield of transrectal prostate biopsies in a 3-T magnetic resonance imaging (MRI) scanner in patients with elevated prostate specific antigen (PSA) levels and recent negative transrectal ultrasonography (TRUS)-guided prostate biopsies. PATIENTS AND METHODS: Between July 2004 and November 2005, patients with at least one previous negative prostate biopsy within the previous 12 months had MRI-guided biopsy of the prostate in a 3-T MRI scanner. Patients with previous positive biopsies for cancer were excluded. Target selection was based on T2-weighted imaging and dynamic contrast-enhanced (DCE) imaging studies. RESULTS: Thirteen patients were eligible; their median (range) age was 61 (47-74) years and PSA value 4.90 (1.3-12.3) ng/mL. Most patients had one previous negative biopsy (range 1-4). Four patients had a family history of prostate cancer. There were 37 distinct targets based on T2-weighted imaging. Fifteen of 16 distinct DCE abnormalities were co-localized with a target based on T2-weighted imaging. Despite this correlation, only one of 13 patients had a directed biopsy positive for cancer. Including systematic biopsies, two of 13 patients had a biopsy positive for prostate cancer. One patient had prostate intraepithelial neoplasia and one had atypical glands in the specimen. CONCLUSION: The prostate-cancer yield of transrectal biopsies in a 3-T MRI scanner, among patients with recent negative TRUS-guided prostate biopsies, is similar to repeat systematic TRUS-guided biopsy. DCE correlates with T2-imaging but does not appear to improve prostate cancer yield in this population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".