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Record W2557050169 · doi:10.1016/j.euf.2016.11.002

68Ga-Labeled Prostate-specific Membrane Antigen Ligand Positron Emission Tomography/Computed Tomography for Prostate Cancer: A Systematic Review and Meta-analysis

2016· review· en· W2557050169 on OpenAlexaff
Finn Edler von Eyben, Maria Picchio, Rie von Eyben, Handoo Rhee, Glenn Bauman

Bibliographic record

VenueEuropean Urology Focus · 2016
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineProstate cancerGlutamate carboxypeptidase IIPositron emission tomographyMeta-analysisProstateMagnetic resonance imagingPositron Emission Tomography-Computed TomographyProstate-specific antigenRadiologyReceiver operating characteristicNuclear medicineBiopsyCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

Context 68 Gallium prostate-specific membrane antigen (PSMA) ligand 68 Ga-HBED-CC-PSMA ( 68 Ga-PSMA) is a promising radiotracer for positron emission tomography (PET)/computed tomography (CT) of prostate cancer. Objective To conduct a meta-analysis to evaluate detection rate, diagnostic test accuracy, and adverse effects of 68 Ga-PSMA PET/CT or PET/magnetic resonance imaging (MRI) for staging of prostate cancer and for restaging of rising prostate-specific antigen (PSA) after initial treatment. Evidence acquisition Following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines, our systematic review searched for articles in PubMed and EMBASE databases from 2012 to July 2016. The reference standard was pathology after biopsy or surgery. The analyses used a random effect model and a hierarchical summary receiver operating characteristic model. Evidence synthesis Fifteen 68 Ga-PSMA PET/CT studies with 1256 patients met the inclusion criteria. Seven studies of staging PET/CT or PET/MRI detected a regional site of cancer for 203 of 273 patients (74%). Nine studies of restaging PET/CT detected sites of recurrence in 799 of 983 patients (81%) with a 50% detection rate (74 of 147 patients) for restaging PSA of 0.2–0.49 ng/ml and a 53% detection rate (56 of 195 patients) for restaging PSA of 0.50–0.99 ng/ml. Staging 68 Ga-PSMA PET/CT in the studies had higher detection rates of sites in the prostate bed than restaging 68 Ga-PSMA PET/CT (mean 57% vs 14%, p =0.031, t test). Both staging and restaging 68 Ga-PSMA PET/CT found that a subgroup of the patients had metastatic sites in pelvic lymph nodes or distant organs. Eight studies of staging PET/CT undertook histologic correlations. We performed prostate-segment-based analysis specifically regarding the primary cancer lesion for four of these studies, and patient-based analysis specifically regarding pelvic lymph node metastases for four other studies. The pooled sensitivities for staging in the two groups of studies were 70% and 61%, and the pooled specificities were 84% and 97%. None of the studies reported complications from the PET/CT imaging. Conclusions 68 Ga-PSMA PET/CT has clinical relevance to detect sites of recurrence for patients with PSA recurrence after radical prostatectomy (RP) with PSA levels less than 1.0 ng/ml. Patient summary Choline positron emission tomography (PET)/computed tomography (CT) can detect sites of recurrent prostate cancer in an earlier phase of prostate-specific antigen (PSA) recurrence than bone scans and CT scans, but choline PET/CT is rarely positive for patients with restaging PSA levels under 1 ng/ml. A new radiotracer called 68 Ga-PSMA for PET/CT was able to detect sites of recurring cancer in up to 50% of patients who had an early rise in PSA exceeding 0.5 ng/ml after initial radical prostatectomy. The published studies did not report adverse effects of 68 Ga-PSMA PET/CT imaging.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.024
Bibliometrics0.0050.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.337
Teacher spread0.286 · 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 designMeta-analysis
Domainnot available
GenreReview

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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Citations255
Published2016
Admission routes1
Has abstractno

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