MP78-02 PREDICTORS OF BIOCHEMICAL RECURRENCE AFTER PRIMARY WHOLE GLAND CRYOTHERAPY FOR PROSTATE CANCER
Notice bibliographique
Résumé
You have accessJournal of UrologyProstate Cancer: Localized: Ablative Therapy (MP78)1 Apr 2019MP78-02 PREDICTORS OF BIOCHEMICAL RECURRENCE AFTER PRIMARY WHOLE GLAND CRYOTHERAPY FOR PROSTATE CANCER Adam Kinnaird*, Ryan McLarty, Alexandra Bain, Ambikaipakan Senthilselvan, Gerald Todd, and Michael Chetner Adam Kinnaird*Adam Kinnaird* More articles by this author , Ryan McLartyRyan McLarty More articles by this author , Alexandra BainAlexandra Bain More articles by this author , Ambikaipakan SenthilselvanAmbikaipakan Senthilselvan More articles by this author , Gerald ToddGerald Todd More articles by this author , and Michael ChetnerMichael Chetner More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557334.94208.3fAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Primary whole gland cryotherapy has been shown to be an effective treatment for clinically localized prostate cancer with multiple RCTs demonstrating similar rates of biochemical recurrence (BCR) compared to radiation therapy. While several studies have assessed risk factors of BCR after radical prostatectomy this data is limited for patients undergoing primary whole gland cryotherapy. We therefore sought to determine specific disease, perioperative and early postoperative variables that modify risk of BCR. METHODS: We performed a retrospective analysis of patients who received primary whole gland cryotherapy between 2007 and 2017 at a large tertiary referral center. The primary outcome was BCR, defined as per the Phoenix criteria (PSA nadir + 2.0 ng/ml). Cox proportional hazard regression analysis was used to test models of variables predicting BCR. The Akaike information criteria (AIC) method was used to model the optimal PSA nadir cut-off for risk of BCR. RESULTS: 350 of 391 patients who received cryotherapy during the study period at our institution were identified as having received primary whole gland cryotherapy. Median follow up time was 38.6 months. BCR occurred in 119 (34%) patients. Age (HR=1.05, p≤0.01) and NCCN risk categories (Intermediate risk: HR=6.11, p=0.07; High risk: HR=12.3, p=0.01; Very high risk: HR=14.9, p=0.01) were found to be independently associated with increased risk of BCR. A PSA nadir ≤0.7 was determined to best predict BCR with PSA 0.7 increasing risk of BCR by a HR=4.36 (p≤0.01). CONCLUSIONS: We have identified several robust disease specific and early postoperative predictors of BCR. These risk factors may be used in counselling patients before their cryoablation as well as for potentially selecting patients who may require closer follow-up based on PSA nadir 0.7 or higher risk features. Source of Funding: None Edmonton, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1142-e1142 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Adam Kinnaird* More articles by this author Ryan McLarty More articles by this author Alexandra Bain More articles by this author Ambikaipakan Senthilselvan More articles by this author Gerald Todd More articles by this author Michael Chetner 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 | 0,001 |
| Bibliométrie | 0,001 | 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) | 0,034 | 0,004 |
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 ».