991 PROSPECTIVE MULTI-INSTITUTIONAL STUDY EVALUATING THE PERFORMANCE OF PROSTATE CANCER RISK CALCULATORS
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».