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Enregistrement W3189544101 · doi:10.1097/ju.0000000000002064.17

MP43-17 DIFFERENCES IN GLEASON SCORE (GS) DISTRIBUTION AND TUMOR AGGRESSIVENESS IN LARGE COHORTS OF ASIAN AND CAUCASIAN MEN

2021· article· en· W3189544101 sur OpenAlexaboutno aff
Liang Dong, Wei Xu, Katherine Lajkosz, Rafael Sánchez-Salas, Dixon Woon, Cynthia Kuk, Yi Zhu, Caio Pasquali Dias dos Santos, Annette Erlich, Zehua Ma, Hongyang Qian, Baijun Dong, Mike Nesbitt, Sigrid Carlsson, Girish S. Kulkarni, Nathan Perlis, Rob Hamilton, Petr Macek, Ants Toi, Antonio Finelli, Neil Fleshner, Xavier Cathelineau, Theodorus van der Kwast, Wei Xue, Alexandre R. Zlotta

Notice bibliographique

RevueThe Journal of Urology · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiomics and Machine Learning in Medical Imaging
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicine

Résumé

récupéré en direct d'OpenAlex

You have accessJournal of UrologyProstate Cancer: Detection & Screening IV (MP43)1 Sep 2021MP43-17 DIFFERENCES IN GLEASON SCORE (GS) DISTRIBUTION AND TUMOR AGGRESSIVENESS IN LARGE COHORTS OF ASIAN AND CAUCASIAN MEN Liang Dong, Wei Xu, Katherine Lajkosz, Rafael Sanchez-Salas, Dixon Woon, Cynthia Kuk, Yi Zhu, Caio Pasquali Dias dos Santos, Annette Erlich, Zehua Ma, Hongyang Qian, Baijun Dong, Mike Nesbitt, Sigrid Carlsson, Girish Kulkarni, Nathan Perlis, Rob Hamilton, Petr Macek, Ants Toi, Antonio Finelli, Neil Fleshner, Xavier Cathelineau, Theodorus van Der Kwast, Wei Xue, and Alexandre R. Zlotta Liang DongLiang Dong More articles by this author , Wei XuWei Xu More articles by this author , Katherine LajkoszKatherine Lajkosz More articles by this author , Rafael Sanchez-SalasRafael Sanchez-Salas More articles by this author , Dixon WoonDixon Woon More articles by this author , Cynthia KukCynthia Kuk More articles by this author , Yi ZhuYi Zhu More articles by this author , Caio Pasquali Dias dos SantosCaio Pasquali Dias dos Santos More articles by this author , Annette ErlichAnnette Erlich More articles by this author , Zehua MaZehua Ma More articles by this author , Hongyang QianHongyang Qian More articles by this author , Baijun DongBaijun Dong More articles by this author , Mike NesbittMike Nesbitt More articles by this author , Sigrid CarlssonSigrid Carlsson More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Nathan PerlisNathan Perlis More articles by this author , Rob HamiltonRob Hamilton More articles by this author , Petr MacekPetr Macek More articles by this author , Ants ToiAnts Toi More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Neil FleshnerNeil Fleshner More articles by this author , Xavier CathelineauXavier Cathelineau More articles by this author , Theodorus van Der KwastTheodorus van Der Kwast More articles by this author , Wei XueWei Xue More articles by this author , and Alexandre R. ZlottaAlexandre R. Zlotta More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002064.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PCa) incidence in Asia is among the lowest in the world, although it has grown rapidly in recent years. We investigated the impact of race [Asian (ASI) or Caucasian (CAU)] on biopsy Gleason Score (GS) distribution in men diagnosed with PCa and compared the clinical outcome of GS 8-10 PCa in ASI and CAU men. METHODS: We first performed a retrospective study of 4969 men with PCa at University Health Network, Toronto, Canada and Renji Hospital, Shanghai, China, between 2014 and 2019, comparing the GS distribution on biopsy between centres. Multivariable logistic regression analyses were performed. To account for difference in GS scoring between centers, we applied a multiple imputation method. We then used Kaplan Meier curves and multivariable Cox proportional hazards models to compare the biochemical and metastasis-free survival between ASI men operated by radical prostatectomy (RadP) for GS8-10 PCa (Shanghai) and CAU men (Institut Montsouris, Paris, France and Toronto). RESULTS: The biopsy study included 2343 vs 2626 men diagnosed with PCa in Shanghai and Toronto, respectively. Median age at diagnosis (70 vs 66 years) and PSA (19.0 vs 7.3 ng/ml) were higher in ASI vs CAU men (p<0.001) whereas their prostates were smaller (39.8cc vs 47.2cc, p<0.001). In 202 biopsies, the kappa coefficient of agreement for GS was 0.71 between centres. On multivariable analysis using the imputation model, adjusting for age, PSA and prostate volume, GS8-10 in ASI was significantly more prevalent on biopsy than in CAU (OR 2.27, 95% CI 2.01-2.57, p<0.001) with comparable results using multivariable logistic regression analysis (OR 2.33, 95% CI 1.92-2.83, p<0.001). In men with PSA<10 ng/ml (n=2387), GS8-10 in ASI was more prevalent on biopsy than in CAU (OR 2.99, 95%CI 2.00-4.48, p<0.001). In 197 ASI men and 199 CAU men operated by RadP for GS 8-10 during the same study period, although the biochemical recurrence free survival was better in CAU (HR from multivariate model 0.54 (95% CI 0.38-0.77), p<0.001), the metastasis-free survival was better in ASI men vs CAU (HR 2.57, 95% CI 1.13-5.87, p=0.024) with 5-year metastasis free survival of 93% (95% CI 0.88-0.98) in ASI vs 78% (85% CI 0.71-0.85), respectively. CONCLUSIONS: Differences in PCa aggressiveness between ASI and CAU men exist, with ASI men more often found with GS8-10. However, the biology of these GS8-10 tumors appears less aggressive in ASI than CAU. Source of Funding: N/A © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e789-e789 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Liang Dong More articles by this author Wei Xu More articles by this author Katherine Lajkosz More articles by this author Rafael Sanchez-Salas More articles by this author Dixon Woon More articles by this author Cynthia Kuk More articles by this author Yi Zhu More articles by this author Caio Pasquali Dias dos Santos More articles by this author Annette Erlich More articles by this author Zehua Ma More articles by this author Hongyang Qian More articles by this author Baijun Dong More articles by this author Mike Nesbitt More articles by this author Sigrid Carlsson More articles by this author Girish Kulkarni More articles by this author Nathan Perlis More articles by this author Rob Hamilton More articles by this author Petr Macek More articles by this author Ants Toi More articles by this author Antonio Finelli More articles by this author Neil Fleshner More articles by this author Xavier Cathelineau More articles by this author Theodorus van Der Kwast More articles by this author Wei Xue More articles by this author Alexandre R. Zlotta 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,268
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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