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Record W4400192544 · doi:10.1001/jamaoncol.2024.3196

Fluorine-18 Prostate-Specific Membrane Antigen–1007 PET/CT vs Multiparametric MRI for Locoregional Staging of Prostate Cancer

2024· article· en· W4400192544 on OpenAlexaffabout
Nikhile Mookerji, Tyler Pfanner, Amaris Hui, Guocheng Huang, Patrick Albers, Rohan Mittal, Stacey Broomfield, Lucas Dean, Blair St. Martin, Niels-Erik Jacobsen, Howard Evans, Yuan Gao, Ryan Hung, Jonathan Abele, Peter Dromparis, Joema Felipe Lima, Tarek A. Bismar, Evangelos D. Michelakis, Gopinath Sutendra, Frank Wuest, Wendy Tu, Benjamin Adam, Christopher Fung, Alexander Tamm, Adam Kinnaird

Bibliographic record

VenueJAMA Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsProstate Cancer CanadaUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineProstate cancerGlutamate carboxypeptidase IIProstateProstate-specific antigenMultiparametric MRINuclear medicineCancerUrologyRadiologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

Importance: Prostate-specific membrane antigen (PSMA) demonstrates overexpression in prostate cancer and correlates with tumor aggressiveness. PSMA positron emission tomography (PET) is superior to conventional imaging for the metastatic staging of prostate cancer per current research but studies of second-generation PSMA PET radioligands for locoregional staging are limited. Objective: To determine the accuracy of fluorine-18 PSMA-1007 PET/computed tomography (18F-PSMA-1007 PET/CT) compared to multiparametric magnetic resonance imaging (MRI) in the primary locoregional staging of intermediate-risk and high-risk prostate cancers. Design, Setting, and Participants: The Next Generation Trial was a phase 2 prospective validating paired cohort study assessing the accuracy of 18F-PSMA-1007 PET/CT and MRI for locoregional staging of prostate cancer, with results of histopathologic examination as the reference standard comparator. Radiologists, nuclear medicine physicians, and pathologists were blinded to preoperative clinical, pathology, and imaging data. Patients underwent all imaging studies and radical prostatectomies at 2 tertiary care hospitals in Alberta, Canada. Eligible participants included men with intermediate-risk or high-risk prostate cancer who consented to radical prostatectomy. Participants who underwent radical prostatectomy were included in the final analysis. Patients were recruited between March 2022 and June 2023, and data analysis occurred between July 2023 and December 2023. Exposures: All participants underwent both 18F-PSMA-1007 PET/CT and MRI within 2 weeks of one another and before radical prostatectomy. Main Outcomes and Measures: The primary outcome was the correct identification of the prostate cancer tumor stage by each imaging test. The secondary outcomes were correct identification of the dominant nodule, laterality, extracapsular extension, and seminal vesical invasion. Results: Of 150 eligible men with prostate cancer, 134 patients ultimately underwent radical prostatectomy (mean [SD] age at prostatectomy, 62.0 [5.7] years). PSMA PET was superior to MRI for the accurate identification of the final pathological tumor stage (61 [45%] vs 38 [28%]; P = .003). PSMA PET was also superior to MRI for the correct identification of the dominant nodule (126 [94%] vs 112 [83%]; P = .01), laterality (86 [64%] vs 60 [44%]; P = .001), and extracapsular extension (100 [75%] vs 84 [63%]; P = .01), but not for seminal vesicle invasion (122 [91%] vs 115 [85%]; P = .07). Conclusions and Relevance: In this phase 2 prospective validating paired cohort study, 18F-PSMA-1007 PET/CT was superior to MRI for the locoregional staging of prostate cancer. These findings support PSMA PET in the preoperative workflow of intermediate-risk and high-risk tumors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.389
Teacher spread0.331 · 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 designNot applicable
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

Citations34
Published2024
Admission routes2
Has abstractyes

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