PD50-01 ASSESSMENT OF MRI PERFORMANCE IN THE CANARY PROSTATE ACTIVE SURVEILLANCE STUDY (PASS)
Notice bibliographique
Résumé
You have accessJournal of UrologyProstate Cancer: Localized: Active Surveillance II (PD50)1 Apr 2019PD50-01 ASSESSMENT OF MRI PERFORMANCE IN THE CANARY PROSTATE ACTIVE SURVEILLANCE STUDY (PASS) Michael Liss*, Michael Garcia, Yingye Zheng, Lisa Newcomb, Christopher Filson, Hilary Boyer, James Brooks, Peter Carroll, Martin Gleave, Francis Martin, Todd Morgan, Peter Nelson, Andrew Wagner, Ian Thompson, and Daniel Lin Michael Liss*Michael Liss* More articles by this author , Michael GarciaMichael Garcia More articles by this author , Yingye ZhengYingye Zheng More articles by this author , Lisa NewcombLisa Newcomb More articles by this author , Christopher FilsonChristopher Filson More articles by this author , Hilary BoyerHilary Boyer More articles by this author , James BrooksJames Brooks More articles by this author , Peter CarrollPeter Carroll More articles by this author , Martin GleaveMartin Gleave More articles by this author , Francis MartinFrancis Martin More articles by this author , Todd MorganTodd Morgan More articles by this author , Peter NelsonPeter Nelson More articles by this author , Andrew WagnerAndrew Wagner More articles by this author , Ian ThompsonIan Thompson More articles by this author , and Daniel LinDaniel Lin More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556876.83918.dbAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: MRI has been shown to increase detection of clinically significant cancer in the initial diagnosis of prostate cancer. We aim to investigate the ability of multiparametric MRI to detect Gleason Grade Group (GG) ≥2 cancer in a multi-institutional active surveillance cohort with standardized follow up and biopsy protocols. METHODS: Men enrolled in PASS across ten institutions were examined to identify men who underwent a biopsy within 12 months of a multiparametric MRI. Local interpretation of MRI PIRADS scores and biopsy GG were used in the analysis. MRI with no lesions or PIRADS 1-3 were considered negative and MRI with PIRADS 4-5 was considered positive. We investigate the performance MRI to detect GG2 or greater disease, controlling for the clinical factors of age, BMI, the proportion of positive cores, prostate size and PSA. We also compared GG found in systematic vs targeted cores in fusion biopsies. RESULTS: We evaluated 351 MRIs from 325 individuals. The negative predictive value (NPV) of MRI for any GG2 or greater was 76% with a false positive rate of 49%. A negative MRI was significant in a multivariable logistic regression (OR 0.55, CI 0.32-0.93; P=0.03). In a sensitivity analysis of 287 MRI in 270 men with only GG1 cancer prior to MRI, biopsy reclassification to GG2 was observed in 27/127 (21%) of negative MRI and 49/139 (35%) of positive MRI. In this subset, negative MRI was not associated with reclassification to GG ≥2 in the multivariable model. In 192 fusion biopsies, GG concordance between the target and systematic biopsies was 81% (156/192). Targeted biopsies identified higher GG than systematic biopsy in 8% (15/192) of men; whereas, systematic biopsy identified higher GG than targeted in 11% (21/192). CONCLUSIONS: While MRI is often used in active surveillance, the NPV of MRI is only 76% and false positive rates may limit the widescale applicability. Systematic biopsy still detects higher GG lesions in 11% suggesting that systematic biopsy cannot be omitted in the setting of positive or negative MRI. Source of Funding: Canary Foundation DoD grant #W81XWH1410595 DoD grant #W81XWH1510441 San Antonio, TX; Seattle, WA; Atlanta, GA; Seattle, WA; Stanford, CA; San Francisco, CA; Vancouver, Canada; Virginia Beach, VA; Ann Arbor, MI; Seattle, WA; Boston, MD; San Antonio, TX; Seattle, WA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e911-e912 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Michael Liss* More articles by this author Michael Garcia More articles by this author Yingye Zheng More articles by this author Lisa Newcomb More articles by this author Christopher Filson More articles by this author Hilary Boyer More articles by this author James Brooks More articles by this author Peter Carroll More articles by this author Martin Gleave More articles by this author Francis Martin More articles by this author Todd Morgan More articles by this author Peter Nelson More articles by this author Andrew Wagner More articles by this author Ian Thompson More articles by this author Daniel Lin More articles by this author Expand All 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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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) | 0,028 | 0,007 |
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 ».