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PD17-09 PROSTATE-SPECIFIC MEMBRANE ANTIGEN POSITRON EMISSION TOMOGRAPHY (PSMA-PET) IN HIGH-RISK NONMETASTATIC CASTRATION-RESISTANT PROSTATE CANCER (NMCRPC) SPARTAN-LIKE PATIENTS (PTS) NEGATIVE BY CONVENTIONAL IMAGING

2019· article· en· W4243844195 on OpenAlexaboutno aff
Boris Hadaschik, Manuel Weber, Amir Iravani, Michael S. Hofman, Jérémie Calais, Johannes Czernin, Harun Ilhan, Fred Saad, Eric J. Small, Matthew R. Smith, Paola M. Perez, Thomas A. Hope, Isabel Rauscher, Anil Londhe, Angela Lopez‐Gitlitz, Shinta Cheng, Tobias Maurer, Ken Herrmann, Matthias Eiber, Wolfgang P. Fendler

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerPositron emission tomographyProstateCancerCastrationOncologyUrologyInternal medicineNuclear medicineHormone

Abstract

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You have accessJournal of UrologyProstate Cancer: Staging II (PD17)1 Apr 2019PD17-09 PROSTATE-SPECIFIC MEMBRANE ANTIGEN POSITRON EMISSION TOMOGRAPHY (PSMA-PET) IN HIGH-RISK NONMETASTATIC CASTRATION-RESISTANT PROSTATE CANCER (NMCRPC) SPARTAN-LIKE PATIENTS (PTS) NEGATIVE BY CONVENTIONAL IMAGING Boris Hadaschik*, Manuel Weber, Amir Iravani, Michael S. Hofman, Jérémie Calais, Johannes Czernin, Harun Ilhan, Fred Saad, Eric J. Small, Matthew R. Smith, Paola M. Perez, Thomas A. Hope, Isabel Rauscher, Anil Londhe, Angela Lopez-Gitlitz, Shinta Cheng, Tobias Maurer, Ken Herrmann, Matthias Eiber, and Wolfgang Fendler Boris Hadaschik*Boris Hadaschik* More articles by this author , Manuel WeberManuel Weber More articles by this author , Amir IravaniAmir Iravani More articles by this author , Michael S. HofmanMichael S. Hofman More articles by this author , Jérémie CalaisJérémie Calais More articles by this author , Johannes CzerninJohannes Czernin More articles by this author , Harun IlhanHarun Ilhan More articles by this author , Fred SaadFred Saad More articles by this author , Eric J. SmallEric J. Small More articles by this author , Matthew R. SmithMatthew R. Smith More articles by this author , Paola M. PerezPaola M. Perez More articles by this author , Thomas A. HopeThomas A. Hope More articles by this author , Isabel RauscherIsabel Rauscher More articles by this author , Anil LondheAnil Londhe More articles by this author , Angela Lopez-GitlitzAngela Lopez-Gitlitz More articles by this author , Shinta ChengShinta Cheng More articles by this author , Tobias MaurerTobias Maurer More articles by this author , Ken HerrmannKen Herrmann More articles by this author , Matthias EiberMatthias Eiber More articles by this author , and Wolfgang FendlerWolfgang Fendler More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555569.70342.60AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: In SPARTAN, pts with nmCRPC assessed by conventional imaging benefited from apalutamide (APA). PSMA-PET detects localized and metastatic PC with superior sensitivity to conventional imaging. We retrospectively characterized the extent of disease using PSMA-PET in SPARTAN-like pts and compared the risk factors for M1 disease detected by PSMA-PET to those in SPARTAN. METHODS: A total of 200 pts with nmCRPC at high risk of developing metastases (prostate-specific antigen doubling time [PSADT] ≤ 10 mo, or Gleason score ≥ 8) and no known extrapelvic metastases on prior conventional imaging were assessed with PSMA-PET. Detection rate on PSMA-PET, including local/pelvic and distant M1 disease, was determined. Association of baseline (BL) variables with M1 disease in the PSMA-PET cohort was assessed using univariate and multivariate analyses. SPARTAN pts were stratified according to risk factors for PSMA-PET-detected M1 disease and analyzed using Cox proportional-hazards models. RESULTS: BL characteristics of PSMA-PET and SPARTAN pts were generally similar. PSMA-PET detected PC in 196/200 (98%) pts; 55% had local recurrence, 54% had pelvic nodes (N1), 55% had any extrapelvic distant metastatic disease despite negative conventional imaging; 24% were diagnosed with local recurrence only, 29% with oligometastatic (1-3 lesions) and 46% with polymetastatic disease. PSA ≥ 5.5 ng/mL, pN1 disease, and prior local therapy were significantly associated with M1 disease detected by PSMA-PET (Table). All clinically relevant subgroups of SPARTAN pts, including pts with independent predictors of PSMA-PET-M1 disease, significantly benefited from APA (Table). CONCLUSIONS: PSMA-PET-positive CRPC pts were similar to those at high-risk of developing metastases in SPARTAN. APA showed significant benefit in all clinically relevant subgroups of SPARTAN pts, including pts with risk factors for distant metastases detected by PSMA-PET. Therefore, APA should be considered for pts negative by conventional imaging but positive by PSMA-PET (stage migration). The added value of PSMA-PET over PSADT in pts with high-risk nmCRPC should be explored in prospective studies. Source of Funding: Janssen Research & Development: SPARTAN. Participating centers: PSMA-PET. Essen, Germany; Melbourne, Australia; Los Angeles, CA; Munich, Germany; Montréal, Canada; San Francisco, CA; Boston, MA; San Francisco, CA; Munich, Germany; Titusville, NJ; Los Angeles, CA; Raritan, NJ; Munich, Germany; Essen, Germany; Munich, Germany; Essen, Germany© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e309-e309 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Boris Hadaschik* More articles by this author Manuel Weber More articles by this author Amir Iravani More articles by this author Michael S. Hofman More articles by this author Jérémie Calais More articles by this author Johannes Czernin More articles by this author Harun Ilhan More articles by this author Fred Saad More articles by this author Eric J. Small More articles by this author Matthew R. Smith More articles by this author Paola M. Perez More articles by this author Thomas A. Hope More articles by this author Isabel Rauscher More articles by this author Anil Londhe More articles by this author Angela Lopez-Gitlitz More articles by this author Shinta Cheng More articles by this author Tobias Maurer More articles by this author Ken Herrmann More articles by this author Matthias Eiber More articles by this author Wolfgang Fendler 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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.264
Teacher spread0.256 · 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".

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Published2019
Admission routes1
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