MP67-12 MRI TARGETED BIOPSY FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS AFTER PRIOR NEGATIVE BIOPSIES.
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Résumé
You have accessJournal of UrologyProstate Cancer: Detection & Screening IV1 Apr 2014MP67-12 MRI TARGETED BIOPSY FOR THE DETECTION OF PROSTATE CANCER IN PATIENTS AFTER PRIOR NEGATIVE BIOPSIES. Hamidreza Abdi, Triona Walshe, Homi Zargar, Farshad Pourmalek, Silvia D. Chang, Martin E. Gleave, Alison C. Harris, Alan I. So, S Larry Goldenberg, Lindsay Machan, and Peter C. Black Hamidreza AbdiHamidreza Abdi More articles by this author , Triona WalsheTriona Walshe More articles by this author , Homi ZargarHomi Zargar More articles by this author , Farshad PourmalekFarshad Pourmalek More articles by this author , Silvia D. ChangSilvia D. Chang More articles by this author , Martin E. GleaveMartin E. Gleave More articles by this author , Alison C. HarrisAlison C. Harris More articles by this author , Alan I. SoAlan I. So More articles by this author , S Larry GoldenbergS Larry Goldenberg More articles by this author , Lindsay MachanLindsay Machan More articles by this author , and Peter C. BlackPeter C. Black More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.2079AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES As technical advancements improve the ability of multi-parametric MRI (mpMRI) of the prostate to detect clinically significant prostate cancer (CaP) while leaving clinically low risk tumors undiagnosed, the clinical application of mpMRI continues to evolve. We aimed to determine the efficacy of mpMRI in the detection of CaP in patients with prior negative transrectal ultrasound-guided prostate biopsy (TRUSBx). METHODS The study was designed as a non-randomized retrospective cohort study. Between January 2010 and September 2013, 2416 men were identified as having had TRUSBx and/or mpMRI at Vancouver General Hospital. Among these, there was a persistent suspicion of CaP in 283 despite prior negative TRUSBx. An MRI was obtained in 112, and a lesion (PIRADS score ≥ 3) was identified in 88 cases (78%). A subsequent MRI-TRUS fusion biopsy (“cognitive” or software-directed (Hologic Inc., Bedford, MA)) in addition to standard template biopsy (8-12 cores depending on prostate volume), was performed in 86 of these 88 cases. From the 171 men who underwent repeat TRUSBx without MRI, a matching cohort of 86 patients was selected using a one-nearest neighbour method without replacement. Matching was based on PSA level, PSA density, prostate volume, and history of ASAP or HGPIN in previous biopsies. The end-point was the detection rate of any CaP or clinically significant CaP (Gleason ≥3+4). Logistic regression analysis was used to determine which factors predicted significant CaP on fusion biopsy. RESULTS Twenty-six patients with mpMRI but no subsequent biopsy were followed for a mean of 14 months without subsequent diagnosis of prostate cancer. Fusion biopsy detected CaP and clinically significant CaP in 36 (42%) and 30 (35%) of men compared to19 (22%) and 14 (16%), respectively, in the men without MRI (p = 0.006 for both). In 9 cases (10%) fusion biopsy detected significant CaP that was missed on standard cores. Significant CaP was present in 5 cases (6%) on standard cores but not the targeted cores. CONCLUSIONS In patients with prior negative biopsy but persistent concern for prostate cancer, MRI enhances the detection of CaP and especially clinically significant CaP. It is possible that this also reduces the number of patients undergoing TRUSBx, although we are uncertain of the true CaP status in the 23% of patients who underwent mpMRI without subsequent TRUSBx. While these results require further validation, we now routinely obtain mpMRI before second TRUSBx. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e753 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Hamidreza Abdi More articles by this author Triona Walshe More articles by this author Homi Zargar More articles by this author Farshad Pourmalek More articles by this author Silvia D. Chang More articles by this author Martin E. Gleave More articles by this author Alison C. Harris More articles by this author Alan I. So More articles by this author S Larry Goldenberg More articles by this author Lindsay Machan More articles by this author Peter C. Black 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 | 0,001 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,016 |
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 ».