1637 IMPACT OF THE 2005 ISUP GLEASON SCORING SYSTEM MODIFICATION ON OUR ABILITY TO PREDICT EXTRAPROSTATIC EXTENSION
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized VIII1 Apr 20121637 IMPACT OF THE 2005 ISUP GLEASON SCORING SYSTEM MODIFICATION ON OUR ABILITY TO PREDICT EXTRAPROSTATIC EXTENSION Carlos Morales, David Margel, Stanley Yap, Michael Nesbitt, Andrew Evans, John Trachtenberg, and Neil Fleshner Carlos MoralesCarlos Morales Toronto, Canada More articles by this author , David MargelDavid Margel Toronto, Canada More articles by this author , Stanley YapStanley Yap Toronto, Canada More articles by this author , Michael NesbittMichael Nesbitt Toronto, Canada More articles by this author , Andrew EvansAndrew Evans Toronto, Canada More articles by this author , John TrachtenbergJohn Trachtenberg Toronto, Canada More articles by this author , and Neil FleshnerNeil Fleshner Toronto, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1454AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Many physicians rely on predictive models such nomograms to provide advice to patients with prostate cancer on best treatment options. In 2005, the International Society of Urological Pathology (ISUP) made the first major revision of the Gleason scoring system since its inception. The aim of our study is to determine whether these modifications impact the relationship between biopsy staging and pathologic findings after radical prostatectomy (RP), as well as to assess the accuracy of Kattan nomograms using the modern scoring system. METHODS We correlated needle biopsy findings with RP pathologic findings in 330 consecutive patients before (years 2002 to 2004) and 690 patients after (years 2008 to 2010) the 2005 ISUP Gleason score modification. All pathological specimens were reviewed by expert uropathologists at our institution. Analysis was stratified across four Gleason grading groups: 6, 7 (3+4), 7 (4+3) and 8 to 10. Other analyzed variables were correlated within each cohort and included tumor volume, surgical margin status, and pathological T Stage. Finally, the Kattan nomogram probability of extraprostatic extension (ECE) was calculated for each patient and the accuracy of the nomogram was compared between the two cohorts using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. RESULTS Overall agreement in Gleason score between biopsy and RP specimens was 67% and 58% for the old and new cohort, respectively (p=0.08). In addition we found a significantly higher rates of downgrading between biopsy and RP specimen in the new cohort (14%) compared to the old cohort (6.5%), p= 0.02. Gleason 7 (3+4) disease identified on biopsy was associated with lower pathologic T Stage when utilizing the new staging system. Among patients with Gleason 7 (3+4) disease on biopsy, rates of pathological T3b disease decreased from 18.5% in the old cohort to 5.9% in the new cohort, while rates of T2 disease increased from 50% to 60.5% (p=0.03). Finally, The older staging system provided better accuracy in predicting ECE using the Kattan nomogram. AUC for the older staging system was 0.78 (95%CI is 0.7-0.84) compared 0.7 (95%CI is 0.67-0.73) in the modified 2005 grading system, p=0.04. CONCLUSIONS We report a decreased concordance between biopsy specimen and final RP pathology when utilizing the new scoring system, attributable to a significant downgrading. The new grading system also demonstrates a decreased ability to predict ECE using Kattan nomograms. These findings need to be considered when risk-stratifying and treament decisions are made. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e661 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Carlos Morales Toronto, Canada More articles by this author David Margel Toronto, Canada More articles by this author Stanley Yap Toronto, Canada More articles by this author Michael Nesbitt Toronto, Canada More articles by this author Andrew Evans 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 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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".