MP51-20 MULTI-PARAMETRIC MRI ENHANCES DETECTION OF SIGNIFICANT TUMOR IN PATIENTS ON ACTIVE SURVEILLANCE FOR PROSTATE CANCER
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized V1 Apr 2014MP51-20 MULTI-PARAMETRIC MRI ENHANCES DETECTION OF SIGNIFICANT TUMOR IN PATIENTS ON ACTIVE SURVEILLANCE FOR PROSTATE CANCER Hamidreza Abdi, Triona Walshe, Farshad Pourmalek, Jonathan Aning, Homi Zargar, Alison C. Harris, Silvia D. Chang, Alan I. So, Martin E. Gleave, Lindsay Machan, Peter C. Black, and S Larry Goldenberg Hamidreza AbdiHamidreza Abdi More articles by this author , Triona WalsheTriona Walshe More articles by this author , Farshad PourmalekFarshad Pourmalek More articles by this author , Jonathan AningJonathan Aning More articles by this author , Homi ZargarHomi Zargar More articles by this author , Alison C. HarrisAlison C. Harris More articles by this author , Silvia D. ChangSilvia D. Chang More articles by this author , Alan I. SoAlan I. So More articles by this author , Martin E. GleaveMartin E. Gleave More articles by this author , Lindsay MachanLindsay Machan More articles by this author , Peter C. BlackPeter C. Black More articles by this author , and S Larry GoldenbergS Larry Goldenberg More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1676AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives A principal limitation of active surveillance (AS) for the management of lower risk prostate cancer (CaP) is undersampling of higher risk tumors, which may be located outside the usual template of a transrectal ultrasound guided biopsy (TRUSBx). Multi-parametric MRI of the prostate (mpMRI-P) offers a method to detect these missed lesions, and fusion biopsy enables sampling of them. Here we investigated the utility of mpMRI-P in patients on AS. Methods We reviewed the charts of 815 patients on AS for localized CaP at the Vancouver Prostate Centre. Of these patients 110 had a mpMRI-P prior to repeat TRUSBx, and selected patients underwent MRI-TRUS fusion biopsy based on the mpMRI-P findings, in addition to a standard biopsy. The results of fusion biopsy cores were compared to the standard biopsy cores, and the role of mpMRI-P in altering patient management was evaluated. Results The median time on AS was 2.47 years (range: 0.6 - 6.9) at the time of the mpMRI-P. Gleason 3+3 cancer was found on initial biopsy in 98 (89%) and Gleason 3+4 in 12 (11%). mpMRI-P detected 112 suspicious lesion in 72 (65%) patients. Of these, 80 (72%) were PIRADS 3 lesions and 32(28%) PIRADS 4 or 5. Cancer and significant cancer (any Gleason pattern 4) were detected in 20 (25%) and 9 (11%) of PIRADS 3 lesions, and in 20 (61%) and 13 (39%) of PIRADS 4/5 lesions, respectively. Fusion biopsy was carried out in 65 of these patients (37 true and 28 cognitive). Gleason grade progression compared to previous biopsy was detected in 11 (10%) patients in the fusion cores, in 7 (6.3%) patients in the standard cores, and in 3 (2.7%) patients in both fusion and standard cores. Two patients discontinued AS due to size increase of a lesion on mpMRI-P. AS was discontinued due to PSA elevation in 1 case and patient choice in 2 cases. mpMRI-P with fusion biopsy was responsible for the determination of disease progression in 13(11.8%) cases. Conclusions These preliminary findings suggest that mpMRI-P with subsequent fusion biopsy enhances the identification of AS patients requiring definitive treatment. Longer follow-up in a larger series of patients is needed to determine the real value of mpMRI-P and subsequent fusion biopsy in the management of patients on AS. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e605 Peer Review Report Advertisement Copyright & Permissions© 2014Metrics Author Information Hamidreza Abdi More articles by this author Triona Walshe More articles by this author Farshad Pourmalek More articles by this author Jonathan Aning More articles by this author Homi Zargar More articles by this author Alison C. Harris More articles by this author Silvia D. Chang More articles by this author Alan I. So More articles by this author Martin E. Gleave More articles by this author Lindsay Machan More articles by this author Peter C. Black More articles by this author S Larry Goldenberg 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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.017 | 0.003 |
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".