991 PROSPECTIVE MULTI-INSTITUTIONAL STUDY EVALUATING THE PERFORMANCE OF PROSTATE CANCER RISK CALCULATORS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection and Screening1 Apr 2011991 PROSPECTIVE MULTI-INSTITUTIONAL STUDY EVALUATING THE PERFORMANCE OF PROSTATE CANCER RISK CALCULATORS Robert Nam, Michael Kattan, Joseph Chin, John Trachtenberg, Rajiv Singal, Ricardo Rendon, Laurence Klotz, Jonathan Izawa, David Bell, Changhong Yu, and Steven Narod Robert NamRobert Nam Toronto, Canada More articles by this author , Michael KattanMichael Kattan Cleveland, OH More articles by this author , Joseph ChinJoseph Chin London, Canada More articles by this author , John TrachtenbergJohn Trachtenberg Toronto, Canada More articles by this author , Rajiv SingalRajiv Singal Toronto, Canada More articles by this author , Ricardo RendonRicardo Rendon Halifax, Canada More articles by this author , Laurence KlotzLaurence Klotz Toronto, Canada More articles by this author , Jonathan IzawaJonathan Izawa London, Canada More articles by this author , David BellDavid Bell Halifax, Canada More articles by this author , Changhong YuChanghong Yu Cleveland, OH More articles by this author , and Steven NarodSteven Narod Toronto, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.1023AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate cancer risk calculators incorporate other risk factors in addition to the prostate specific antigen (PSA) test to evaluate an individual's risk for having prostate cancer. We validated two common North American-based, prostate cancer risk calculators. METHODS We conducted a prospective, multi-institutional study of 2130 patients who underwent a prostate biopsy for prostate cancer detection from five prostate biopsy centres. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator and the Prostate Cancer Prevention Trial (PCPT)-based risk calculator to predict the presence of any and high-grade prostate cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models. RESULTS Of the 2130 patients, 867 (40.7%) men were found to have cancer, and 1263 (59.3%) did not. Of the patients with cancer, 403 (46.5%) had Gleason Score 7 or more cancer. The area under the curve (AUC) for the Sunnybrook risk calculator for predicting prostate cancer was 0.67 (95% C.I.: 0.65–0.69); the AUC for the PCPT risk calculator was 0.61 (95% C.I.: 0.59–0.64). The AUC was also higher for predicting aggressive disease from the Sunnybrook risk calculator (0.72, 95% C.I.: 0.70–0.75), compared to the PCPT risk calculator (0.67, 95% C.I.: 0.64–0.70). Decision-curve analyses also showed the Sunnybrook risk calculator to have better performance than the PCPT risk calculator over a large range of threshold probabilities. CONCLUSIONS The Sunnybrook nomogram-based prostate cancer risk calculator preformed better than the PCPT-based calculator, particularly for assessing the presence of aggressive cancer. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e399-e400 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Robert Nam Toronto, Canada More articles by this author Michael Kattan Cleveland, OH More articles by this author Joseph Chin London, Canada More articles by this author John Trachtenberg Toronto, Canada More articles by this author Rajiv Singal Toronto, Canada More articles by this author Ricardo Rendon Halifax, Canada More articles by this author Laurence Klotz Toronto, Canada More articles by this author Jonathan Izawa London, Canada More articles by this author David Bell Halifax, Canada More articles by this author Changhong Yu Cleveland, OH More articles by this author Steven Narod 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.002 |
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".