MétaCan
Menu
← Back to cohort

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

2021· article· en· W3189544101 on 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

Bibliographic record

VenueThe Journal of Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from 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 ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.268
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Urology→Same topicRadiomics and Machine Learning in Medical Imaging→French-language works237,207→