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Record W4388752480 · doi:10.1016/j.clgc.2023.11.006

The Prognostic Role of Preoperative PSMA PET/CT in cN0M0 pN+ Prostate Cancer: A Multicenter Study

2023· article· en· W4388752480 on OpenAlexaff
Giancarlo Marra, Paweł Rajwa, Claudia Filippini, Guillaume Ploussard, Gabriele Montefusco, Ignacio Puche‐Sanz, Jonathan Olivier, Fabio Zattoni, Fabrizio Moro, Alessandro Magli, Charles Dariane, Andres Affentranger, Josias Bastian Grogg, Thomas Hermanns, Peter Ka‐Fung Chiu, Bartosz Małkiewicz, Kamil Kowalczyk, Roderick C.N. van den Bergh, Shahrokh F. Shariat, Alberto Bianchi, Alessandro Antonelli, Sebastian Gallina, William Berchiche, Rafael Sanchez‐Salas, Xavier Cathelineau, Luca Afferi, Christian D. Fankhauser, Agostino Mattei, R. Jeffrey Karnes, Simone Scuderi, Francesco Montorsi, Alberto Briganti, Désirèe Deandreis, Paolo Gontero, Giorgio Gandaglia, Facco Matteo, Fabrizio Tonetto, Timo Soeterik

Bibliographic record

VenueClinical Genitourinary Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineProstate cancerProstatectomyLymph nodeBiochemical recurrenceRadiologyPET-CTProportional hazards modelNuclear medicinePositron emission tomographyInternal medicineCancer

Abstract

fetched live from OpenAlex

ContextDespite negative preoperative conventional imaging, up to 10% of patients with prostate cancer (PCa) harbor lymph-node involvement (LNI) at radical prostatectomy (RP). The advent of more accurate imaging modalities such as PET/CT improved the detection of LNI. However, their clinical impact and prognostic value are still unclear. We aimed to investigate the prognostic value of pre-operative PET/CT in patients node positive (pN+) at RP.Evidence synthesisWe retrospectively identified cN0M0 patients at conventional imaging (CT and/or MRI, and bone scan) who had pN+ PCa at RP at 17 referral centers. Patients with cN+ at PSMA/Choline PET/CT but cN0M0 at conventional imaging were also included. Systemic progression/recurrence was the primary outcome; Cox proportional hazards models were used for multivariate analysis.Evidence acquisitionWe included 1,163 pN+ men out of whom 95 and 100 had pre-operative PSMA and/or Choline PET/CT, respectively. ISUP grade ≥4 was detected in 66.6%. Overall, 42% of patients had post-operative PSA persistence (≥0.1 ng/mL). Post-operative management included initial observation (34%), ADT (22.7%) and adjuvant RT+/-ADT (42.8%). Median follow-up was 42 months. Patients with cN+ on PSMA PET/CT had an increased risk of systemic progression (52.9% vs 13.6% cN0 PSMA PET/CT vs 27% cN0 at conventional imaging; p <0.01). This held true at multivariable analysis: (HR 5.179, 95% CI 2.781-9.645; p <0.001) whilst no significant results were highlighted for Choline PET/CT. No significant associations for both PET types were found for local progression, BCR, and overall mortality (all p >0.05). Observation as an initial management strategy instead of adjuvant treatments was related with an increased risk of metastases (HR 1.804; 95% CI: 1.045-3.113; p <0.05).ConclusionsPSMA PET/CT cN+ patients with negative conventional imaging have an increased risk of systemic progression after RP compared to their counterparts with cN0M0 disease both at conventional and/or molecular imaging.Micro-AbstractPET/CT have improved the detection of lymph node involvement in patients with prostate cancer at staging. We aimed to investigate the prognostic value of preoperative PET/CT in patients with node negative at conventional imaging and node positive at radical prostatectomy (RP). We included 1,163 patients with these features from 17 referral centers. 95 and 100 patients had pre-operative PSMA and/or Choline PET/CT, respectively. Node positive patients at PSMA PET/CT with negative conventional imaging have an increased risk of systemic progression after RP compared to node negative patients both at conventional and/or molecular imaging. No significant results were highlighted for Choline PET/CT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.400
Teacher spread0.354 · 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 teacher head, 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

Citations6
Published2023
Admission routes1
Has abstractyes

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