The role of MRI in detecting clinically significant prostate cancer - the Manitoba experience
Bibliographic record
Abstract
OBECTIVES: To examine magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) fusion biopsies completed within Manitoba and evaluate their ability in detecting clinically significant prostate cancer. PATIENTS & METHODS: 200 cases from October 2021 and May 2022 that met the inclusion criteria were reviewed. Clinical and pathologic information was collected for each case. The cancer detection rate (CDR) and clinically significant CDR were calculated for systematic and targeted prostate biopsy cores for patients that underwent MRIs at Health Sciences Centre (HSC) or St. Boniface General Hospital (SBGH). Clinically significant prostate cancer was set as all cancers with a Gleason score ≥4+3/Grade Group ≥3 or Gleason score 3+4/Grade Group 2 with cribriform and/or intraductal carcinoma present. RESULTS: The overall CDR including both systematic and targeted biopsy cores was determined to be 75.0%, with an overall CDR for targeted biopsies of 70.5%. The targeted biopsy cores had an overall clinically significant CDR of 34.0%. Prostate cancer was detected in only the targeted biopsies in 38 of the 200 cases, with clinically significant prostate cancer detected in 12 of those cases compared to 9 cases that detected cancer in the systematic biopsies only, 3 of which were clinically significant. When stratified by MRI site, SBGH had a higher targeted biopsy CDR (76.1%) and clinically significant targeted biopsy CDR (35.1%) compared to HSC (59.1% and 31.8%). Nine of the 18 radical prostatectomies had no cancer detected in the systematic biopsies. CONCLUSIONS: This study has established that MRI-TRUS fusion biopsies are effective in detecting additional clinically significant prostate adenocarcinomas in Manitoba.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".