PD50-01 ASSESSMENT OF MRI PERFORMANCE IN THE CANARY PROSTATE ACTIVE SURVEILLANCE STUDY (PASS)
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized: Active Surveillance II (PD50)1 Apr 2019PD50-01 ASSESSMENT OF MRI PERFORMANCE IN THE CANARY PROSTATE ACTIVE SURVEILLANCE STUDY (PASS) Michael Liss*, Michael Garcia, Yingye Zheng, Lisa Newcomb, Christopher Filson, Hilary Boyer, James Brooks, Peter Carroll, Martin Gleave, Francis Martin, Todd Morgan, Peter Nelson, Andrew Wagner, Ian Thompson, and Daniel Lin Michael Liss*Michael Liss* More articles by this author , Michael GarciaMichael Garcia More articles by this author , Yingye ZhengYingye Zheng More articles by this author , Lisa NewcombLisa Newcomb More articles by this author , Christopher FilsonChristopher Filson More articles by this author , Hilary BoyerHilary Boyer More articles by this author , James BrooksJames Brooks More articles by this author , Peter CarrollPeter Carroll More articles by this author , Martin GleaveMartin Gleave More articles by this author , Francis MartinFrancis Martin More articles by this author , Todd MorganTodd Morgan More articles by this author , Peter NelsonPeter Nelson More articles by this author , Andrew WagnerAndrew Wagner More articles by this author , Ian ThompsonIan Thompson More articles by this author , and Daniel LinDaniel Lin More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556876.83918.dbAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: MRI has been shown to increase detection of clinically significant cancer in the initial diagnosis of prostate cancer. We aim to investigate the ability of multiparametric MRI to detect Gleason Grade Group (GG) ≥2 cancer in a multi-institutional active surveillance cohort with standardized follow up and biopsy protocols. METHODS: Men enrolled in PASS across ten institutions were examined to identify men who underwent a biopsy within 12 months of a multiparametric MRI. Local interpretation of MRI PIRADS scores and biopsy GG were used in the analysis. MRI with no lesions or PIRADS 1-3 were considered negative and MRI with PIRADS 4-5 was considered positive. We investigate the performance MRI to detect GG2 or greater disease, controlling for the clinical factors of age, BMI, the proportion of positive cores, prostate size and PSA. We also compared GG found in systematic vs targeted cores in fusion biopsies. RESULTS: We evaluated 351 MRIs from 325 individuals. The negative predictive value (NPV) of MRI for any GG2 or greater was 76% with a false positive rate of 49%. A negative MRI was significant in a multivariable logistic regression (OR 0.55, CI 0.32-0.93; P=0.03). In a sensitivity analysis of 287 MRI in 270 men with only GG1 cancer prior to MRI, biopsy reclassification to GG2 was observed in 27/127 (21%) of negative MRI and 49/139 (35%) of positive MRI. In this subset, negative MRI was not associated with reclassification to GG ≥2 in the multivariable model. In 192 fusion biopsies, GG concordance between the target and systematic biopsies was 81% (156/192). Targeted biopsies identified higher GG than systematic biopsy in 8% (15/192) of men; whereas, systematic biopsy identified higher GG than targeted in 11% (21/192). CONCLUSIONS: While MRI is often used in active surveillance, the NPV of MRI is only 76% and false positive rates may limit the widescale applicability. Systematic biopsy still detects higher GG lesions in 11% suggesting that systematic biopsy cannot be omitted in the setting of positive or negative MRI. Source of Funding: Canary Foundation DoD grant #W81XWH1410595 DoD grant #W81XWH1510441 San Antonio, TX; Seattle, WA; Atlanta, GA; Seattle, WA; Stanford, CA; San Francisco, CA; Vancouver, Canada; Virginia Beach, VA; Ann Arbor, MI; Seattle, WA; Boston, MD; San Antonio, TX; Seattle, WA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e911-e912 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Michael Liss* More articles by this author Michael Garcia More articles by this author Yingye Zheng More articles by this author Lisa Newcomb More articles by this author Christopher Filson More articles by this author Hilary Boyer More articles by this author James Brooks More articles by this author Peter Carroll More articles by this author Martin Gleave More articles by this author Francis Martin More articles by this author Todd Morgan More articles by this author Peter Nelson More articles by this author Andrew Wagner More articles by this author Ian Thompson More articles by this author Daniel Lin More articles by this author Expand All 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".