Transurethral (TURP) biopsy of suspected anterior prostate cancers identified by MRI: Pilot study of a novel technique.
Bibliographic record
Abstract
230 Background: Prostatic MRI allows for identification of cancerous lesions in patients with a prior negative TRUS biopsy. Although in-gantry and MR-ultrasound fusion technologies have been reported, access to these technologies is limited. Here we present a novel technique to sample anterior lesions suspicious for prostate cancer (PCa) identified by MRI with transurethral (TURP) biopsy. Methods: MRI studies ordered by a single uro-oncologist at a tertiary center from 2007 to 2013 were reviewed. Patients were included if their studies met the following criteria: prostate MRI (n=148), indication for MRI was prior negative TRUS biopsy (n=48), PCa considered likely by reading radiologist (n=25) and anterior tumor location (n=7). Anterior tumors were those 17mm or more from the posterior prostate margin. Medical records were thoroughly reviewed. Additionally, regional medical databases were searched for 30 day readmission, emergency room (ER) visit, or positive bacterial culture result. Biopsy patients were treated with the standard monopolar TURP clinical pathway at our center. TURP sampling was directed by pre-procedural review of MRI images and report by the surgeon. MRI imaging was read by a single radiologist and obtained with a 3-T MRI with no endorectal coil employing T1, T2, DWI, and DCE sequences. Results: All seven patients identified with anterior tumors received TURP biopsy. Patients had a mean age of 63.6, Prostate-specific antigen of 13.3 ng/mL and a mean of 2.3 prior negative TRUS biopsies. There were no immediate complications reported. In 30 day follow-up data, no patients had a postoperative infection, although one patient was assessed in the ER for self-resolving hematuria and one patient was admitted for hematuria. Mean specimen weight was 4g and all but one patient had Gleason 7 or higher PCa on biopsy. This study demonstrated that MRI followed by TURP biopsy for anterior PCa had an 85.7% cancer detection rate for clinically significant anterior cancers. Conclusions: This pilot study demonstrates that cognitively directed TURP biopsy for MRI-identified anterior prostate lesions is feasible, has an excellent pickup rate for clinically significant cancer, and does not require access to limited technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".