MP81-10 RISK STRATIFICATION FOR EQUIVOCAL PI-RADS 3 RESULTS: CAN MICRO-ULTRASOUND HELP DETERMINE WHICH MEN TO BIOPSY?
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Résumé
You have accessJournal of UrologyProstate Cancer: Detection & Screening VIII (MP81)1 Apr 2020MP81-10 RISK STRATIFICATION FOR EQUIVOCAL PI-RADS 3 RESULTS: CAN MICRO-ULTRASOUND HELP DETERMINE WHICH MEN TO BIOPSY? Georg Salomon*, Giovanni Lughezzani, Hannes Cash, Laura Wiemer, Robin Heckmann, Sebastian Hofbauer, Ander Astobieta, Andrea Sánchez, Frédéric Staerman, Laurent Lopez, Richard Gaston, Thierry Piéchaud, Gregg Eure, Eric Klein, Robert Abouassaly, and Sangeet Ghai Georg Salomon*Georg Salomon* More articles by this author , Giovanni LughezzaniGiovanni Lughezzani More articles by this author , Hannes CashHannes Cash More articles by this author , Laura WiemerLaura Wiemer More articles by this author , Robin HeckmannRobin Heckmann More articles by this author , Sebastian HofbauerSebastian Hofbauer More articles by this author , Ander AstobietaAnder Astobieta More articles by this author , Andrea SánchezAndrea Sánchez More articles by this author , Frédéric StaermanFrédéric Staerman More articles by this author , Laurent LopezLaurent Lopez More articles by this author , Richard GastonRichard Gaston More articles by this author , Thierry PiéchaudThierry Piéchaud More articles by this author , Gregg EureGregg Eure More articles by this author , Eric KleinEric Klein More articles by this author , Robert AbouassalyRobert Abouassaly More articles by this author , and Sangeet GhaiSangeet Ghai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000973.010AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Reducing unnecessary prostate biopsy procedures is an important clinical goal to reduce pain and anxiety for the patient, as well as the risk of infection and overtreatment. Multiparametric (mpMRI) has been proposed as an effective strategy to reduce the need for a prostate biopsy both in the initial and in the repeat biopsy setting. However, indeterminate or equivocal findings at mpMRI can pose a diagnostic challenge. We aimed to determine whether micro-ultrasound could help to further stratify the need for a prostate biopsy in patients with PIRADS 3 lesion at mpMRI. METHODS: This study was based on a retrospective series of patients presenting with at least one PI-RADS 3 lesion at mpMRI at one of 7 international sites. All patients were imaged with micro-ultrasound using the ExactVu™ (Exact Imaging, Markham, Canada) system, and the presence of suspicious lesions was determined and graded according to the PRI-MUS™ (Prostate risk identification using micro-ultrasound)1 protocol. Maximum PRI-MUS score for each subject was used to determine whether the case was non-suspicious on micro-ultrasound (PRI-MUS 1 or 2), equivocal (PRI-MUS 3), or suspicious (PRI-MUS 4 or 5). All patients with a suspicious (PRI-MUS >2) lesion were subjected to a micro-ultrasound guided targeted biopsy. In addition, mpMRI targeted biopsies on PI-RADS 3 lesions were also obtained either with a cognitive or with a fusion biopsy technique according to each center protocol. RESULTS: 144 subjects were included. Overall prostate cancer detection rate for PI-RADS 3 subjects was 48% (69/144), while 20% (29/144) of patients were diagnosed with a clinically significant prostate cancer defined as a ISUP Grade Group (GG) > 1 tumor. PRI-MUS was able to provide significant risk stratification in this population, with non-suspicious micro-ultrasound imaging reducing the risk of finding GG>1 cancer by more than half to 5% (1/19). Equivocal micro-ultrasound provided little additional information with a GG>1 detection rate of 14% (5/35), while suspicious micro-ultrasound imaging resulted in a significant 17% relative increase in GG>1 detection rate to 26% (29/90, p=0.02). CONCLUSIONS: Micro-ultrasound imaging and PRI-MUS protocol findings appear to provide useful additional information in the case of equivocal mpMRI results. When combined with other clinical risk indicators such as PSA, PSA density and family history, it may be possible to better advise patients on the necessity of a biopsy using this data. Source of Funding: None. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1240-e1240 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Georg Salomon* More articles by this author Giovanni Lughezzani More articles by this author Hannes Cash More articles by this author Laura Wiemer More articles by this author Robin Heckmann More articles by this author Sebastian Hofbauer More articles by this author Ander Astobieta More articles by this author Andrea Sánchez More articles by this author Frédéric Staerman More articles by this author Laurent Lopez More articles by this author Richard Gaston More articles by this author Thierry Piéchaud More articles by this author Gregg Eure More articles by this author Eric Klein More articles by this author Robert Abouassaly More articles by this author Sangeet Ghai 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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,022 |
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 ».