1498 ACTIVE SURVEILLANCE PROSTATE RE-BIOPSY SCHEMA: RESULTS OF ROUTINE TRANSITION ZONE BIOPSIES
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Abstract
You have accessJournal of UrologyProstate Cancer: Localized (VII)1 Apr 20131498 ACTIVE SURVEILLANCE PROSTATE RE-BIOPSY SCHEMA: RESULTS OF ROUTINE TRANSITION ZONE BIOPSIES Lih-Ming Wong, Greg Trottier, Ants Toi, Alexandre Zlotta, Narhari Timilshina, Andrew Evans, Theo Van der Kwast, Michael Jewett, Girish Kulkarni, Robert Hamilton, John Trachtenberg, Neil Fleshner, and Antonio Finelli Lih-Ming WongLih-Ming Wong Toronto, Canada , Greg TrottierGreg Trottier Toronto, Canada , Ants ToiAnts Toi Toronto, Canada , Alexandre ZlottaAlexandre Zlotta Toronto, Canada , Narhari TimilshinaNarhari Timilshina Toronto, Canada , Andrew EvansAndrew Evans Toronto, Canada , Theo Van der KwastTheo Van der Kwast Toronto, Canada , Michael JewettMichael Jewett Toronto, Canada , Girish KulkarniGirish Kulkarni Toronto, Canada , Robert HamiltonRobert Hamilton Toronto, Canada , John TrachtenbergJohn Trachtenberg Toronto, Canada , Neil FleshnerNeil Fleshner Toronto, Canada , and Antonio FinelliAntonio Finelli Toronto, Canada View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2977AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate re-biopsy schema for most published active surveillance (AS) cohorts is based on a standard extended template sampling the peripheral zone (PZ). In an AS population, we investigate the effect of routine transition zone (TZ) biopsy by examining the amount of TZ cancer found and its subsequent impact on pathological re-classification. METHODS Patients were identified from our tertiary referral centre AS database (1997-2012). Eligibility criteria included PSA <10, clinical stage ≤2, Gleason score (GS) ≤6, number of positive cores (PCore) ≤3, no single core >50% involved, age ≤75 years and at least 1 prostatic re-biopsy after diagnosis. Biopsies of the TZ were taken routinely after the diagnostic biopsy (B1). By mapping location of all PCores found, patients with cancer and pathological re-classification in the TZ could be identified at each AS biopsy. As the number of TZ cores taken is usually limited to 1-2, pathological re-classification for TZ cancers was defined as GS≥7 and/or >50% single core involved. Number of PCores was not used. Logistical regression was performed to identify features at diagnostic biopsy that could predict TZ-only reclassification on the 2nd, otherwise known as the confirmatory biopsy (B2). RESULTS The frequency of cancer, and reclassification, found in the TZ during subsequent biopsies during AS is shown in Table 1. At each re-biopsy, there was a consistent proportion of men that had TZ cancer detected (13.5-16.6%), and TZ re-classification (7.1-9.8%). After excluding men who re-classified in both TZ and PZ, the frequency of TZ-only re-classification at each biopsy was 4.8-8.4%. There was a total 64 TZ-only re-classification events in our cohort. Breakdown of re-classification type was: >50% single core involved (n=47), GS≥7 (n=12), both criteria (n=5). Of the 17 men with TZ grade-related re-classification, most were GS=3+4 (n=15), with few GS4+3 (n=1) and GS 4+4 (n=1). On univariate analysis, only % of core involved at B1 was predictive of TZ-only re-classification at B2 (OR1.04, 1.01-1.09, p=0.008). CONCLUSIONS Routine TZ biopsy during AS detects cancer in ∼15% of men at each biopsy. However, only 5% of men at each biopsy re-classify in only in the TZ, and most of these are with GS 3+4 disease. Our results suggest TZ biopsy could be performed less frequently or even not performed at all during routine AS re-biopsy. Frequency of TZ cancer and TZ re-classification found at each subsequent biopsy during AS Biopsy Number Total number of patients having biopsy Total number of patients that re-classified on biopsy (⁎) Number of patients with cancer found in the TZ Number of patients that had re-classification in the TZ Number of patients that had TZ-only re-classification (#) Biopsy 2 (̂) 622 145 103 (16.6%, 103/622) 58 (9.3%, 58/622) 35 (5.6%, 35/622) Biopsy 3 318 63 43 (13.5%) 23 (7.2%) 16 (5.0%) Biopsy 4 131 21 20 (15.2%) 13 (9.9%) 11 (8.4%) Biopsy 5 42 5 6 (14.3%) 3 (7.1%) 2 (4.8%) Total 172 97 64 ⁎ Re-classification defined as GS > 6, > 50single core involved, number of positive cores > 3. # Number of positive cores not used. (̂) Biopsy 2, otherwise known as the confirmatory biopsy. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e614 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lih-Ming Wong Toronto, Canada More articles by this author Greg Trottier Toronto, Canada More articles by this author Ants Toi Toronto, Canada More articles by this author Alexandre Zlotta Toronto, Canada More articles by this author Narhari Timilshina Toronto, Canada More articles by this author Andrew Evans Toronto, Canada More articles by this author Theo Van der Kwast Toronto, Canada More articles by this author Michael Jewett Toronto, Canada More articles by this author Girish Kulkarni Toronto, Canada More articles by this author Robert Hamilton Toronto, Canada More articles by this author John Trachtenberg Toronto, Canada More articles by this author Neil Fleshner Toronto, Canada More articles by this author Antonio Finelli Toronto, Canada 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
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".