MP67-12 MRI TARGETED BIOPSY FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS AFTER PRIOR NEGATIVE BIOPSIES.
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening IV1 Apr 2014MP67-12 MRI TARGETED BIOPSY FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS AFTER PRIOR NEGATIVE BIOPSIES. Hamidreza Abdi, Triona Walshe, Homi Zargar, Farshad Pourmalek, Silvia D. Chang, Martin E. Gleave, Alison C. Harris, Alan I. So, S Larry Goldenberg, Lindsay Machan, and Peter C. Black Hamidreza AbdiHamidreza Abdi More articles by this author , Triona WalsheTriona Walshe More articles by this author , Homi ZargarHomi Zargar More articles by this author , Farshad PourmalekFarshad Pourmalek More articles by this author , Silvia D. ChangSilvia D. Chang More articles by this author , Martin E. GleaveMartin E. Gleave More articles by this author , Alison C. HarrisAlison C. Harris More articles by this author , Alan I. SoAlan I. So More articles by this author , S Larry GoldenbergS Larry Goldenberg More articles by this author , Lindsay MachanLindsay Machan More articles by this author , and Peter C. BlackPeter C. Black More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.2079AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES As technical advancements improve the ability of multi-parametric MRI (mpMRI) of the prostate to detect clinically significant prostate cancer (CaP) while leaving clinically low risk tumors undiagnosed, the clinical application of mpMRI continues to evolve. We aimed to determine the efficacy of mpMRI in the detection of CaP in patients with prior negative transrectal ultrasound-guided prostate biopsy (TRUSBx). METHODS The study was designed as a non-randomized retrospective cohort study. Between January 2010 and September 2013, 2416 men were identified as having had TRUSBx and/or mpMRI at Vancouver General Hospital. Among these, there was a persistent suspicion of CaP in 283 despite prior negative TRUSBx. An MRI was obtained in 112, and a lesion (PIRADS score ≥ 3) was identified in 88 cases (78%). A subsequent MRI-TRUS fusion biopsy (“cognitive” or software-directed (Hologic Inc., Bedford, MA)) in addition to standard template biopsy (8-12 cores depending on prostate volume), was performed in 86 of these 88 cases. From the 171 men who underwent repeat TRUSBx without MRI, a matching cohort of 86 patients was selected using a one-nearest neighbour method without replacement. Matching was based on PSA level, PSA density, prostate volume, and history of ASAP or HGPIN in previous biopsies. The end-point was the detection rate of any CaP or clinically significant CaP (Gleason ≥3+4). Logistic regression analysis was used to determine which factors predicted significant CaP on fusion biopsy. RESULTS Twenty-six patients with mpMRI but no subsequent biopsy were followed for a mean of 14 months without subsequent diagnosis of prostate cancer. Fusion biopsy detected CaP and clinically significant CaP in 36 (42%) and 30 (35%) of men compared to19 (22%) and 14 (16%), respectively, in the men without MRI (p = 0.006 for both). In 9 cases (10%) fusion biopsy detected significant CaP that was missed on standard cores. Significant CaP was present in 5 cases (6%) on standard cores but not the targeted cores. CONCLUSIONS In patients with prior negative biopsy but persistent concern for prostate cancer, MRI enhances the detection of CaP and especially clinically significant CaP. It is possible that this also reduces the number of patients undergoing TRUSBx, although we are uncertain of the true CaP status in the 23% of patients who underwent mpMRI without subsequent TRUSBx. While these results require further validation, we now routinely obtain mpMRI before second TRUSBx. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e753 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Hamidreza Abdi More articles by this author Triona Walshe More articles by this author Homi Zargar More articles by this author Farshad Pourmalek More articles by this author Silvia D. Chang More articles by this author Martin E. Gleave More articles by this author Alison C. Harris More articles by this author Alan I. So More articles by this author S Larry Goldenberg More articles by this author Lindsay Machan More articles by this author Peter C. Black More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".