PD55-06 COMPARISON OF MRI/FUSION VERSUS TRUS CONFIRMATORY BIOPSY IN ACTIVE SURVEILLANCE PATIENTS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized: Active Surveillance III (PD55)1 Apr 2019PD55-06 COMPARISON OF MRI/FUSION VERSUS TRUS CONFIRMATORY BIOPSY IN ACTIVE SURVEILLANCE PATIENTS David Feng, Jeremiah R Dallmer, Svetlana Avulova*, Amy N Luckenbaugh, Aaron A Laviana, Sam S Chang, David F Penson, Matthew J Resnick, Kristen R Scarpato, and Daniel A Barocas David FengDavid Feng More articles by this author , Jeremiah R DallmerJeremiah R Dallmer More articles by this author , Svetlana Avulova*Svetlana Avulova* More articles by this author , Amy N LuckenbaughAmy N Luckenbaugh More articles by this author , Aaron A LavianaAaron A Laviana More articles by this author , Sam S ChangSam S Chang More articles by this author , David F PensonDavid F Penson More articles by this author , Matthew J ResnickMatthew J Resnick More articles by this author , Kristen R ScarpatoKristen R Scarpato More articles by this author , and Daniel A BarocasDaniel A Barocas More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557073.87014.7fAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Selection of appropriate patients for active surveillance (AS) is a key safety issue for conservative management of low-risk localized prostate cancer. Confirmatory biopsy is often performed within a year of diagnosis in order to identify occult high-grade or high-volume cancer, and magnetic resonance imaging (MRI) has been increasingly used to guide confirmatory biopsies. Its overall utility versus conventional transrectal ultrasound (TRUS) biopsy, however, remains unknown. Therefore, we sought to determine the degree to which each technique identified pathologic features that rendered patients ineligible for AS on confirmatory biopsy. METHODS: Cohort consisted of 645 men who chose AS and underwent confirmatory biopsy between 2010 and 2018 with either a repeat TRUS biopsy (575 men) or MRI fusion biopsy (70 men) (which included 12 standard cores in addition to targeted cores). Our primary outcome of interest was AS ineligibility on confirmatory biopsy as defined using the previously published eligibility definitions from 4 major AS cohorts (John Hopkins University [JHU], University of Toronto, Canary Prostate Active Surveillance Study [PASS], and University of California San Francisco [UCSF]). RESULTS: Patients who underwent MRI had a higher average number of cores taken (16.62 versus 14.48, p=0.0001) and larger maximum percent core involvement (22.19% versus 15.34%, p=0.015). In the TRUS protocol, 8-17% of men were no longer eligible for AS after a confirmatory biopsy, whereas, in the MRI protocol, 10-32% were ineligible (Table). JHU AS criteria are the most strict, and in our cohort resulted in the highest rate of ineligibility for AS. Amongst the 4 AS cohorts, MRI confirmatory biopsy was significantly more likely to rule a patient ineligible for AS according to the JHU eligibility criteria (32% versus 17%, respectively, p=0.040), with no significant difference in the other cohorts. CONCLUSIONS: When adhering to strict inclusion criteria for AS, use of MRI-TRUS fusion for confirmatory biopsy results in an increased rate of ineligibility. When applying more inclusive criteria, MRI use increases the number of cores taken and maximum percent tumor involvement per core, without altering eligibility. Thus, MRI-TRUS fusion biopsy may have utility if the intent is to remain on AS only if still meeting JHU criteria after confirmatory biopsy. Source of Funding: None Nashville , TN; Nashville, TN© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1013-e1014 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information David Feng More articles by this author Jeremiah R Dallmer More articles by this author Svetlana Avulova* More articles by this author Amy N Luckenbaugh More articles by this author Aaron A Laviana More articles by this author Sam S Chang More articles by this author David F Penson More articles by this author Matthew J Resnick More articles by this author Kristen R Scarpato More articles by this author Daniel A Barocas 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".