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Record W7073608225

Comparison of Multiparametric Magnetic Resonance Imaging-Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer A Phase 3 Randomized Clinical Trial

2021· article· en· W7073608225 on OpenAlexaboutno aff

Bibliographic record

VenueUCL Discovery (University College London) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerBiopsyContraindicationTransrectal ultrasonographyMagnetic resonance imagingProstateProstate biopsy
DOInot available

Abstract

fetched live from OpenAlex

IMPORTANCE: Magnetic resonance imaging (MRI) with targeted biopsy is an appealing alternative to systematic 12-core transrectal ultrasonography (TRUS) biopsy for prostate cancer diagnosis, but has yet to be widely adopted. \n \nOBJECTIVE: To determine whether MRI with only targeted biopsy was noninferior to systematic TRUS biopsies in the detection of International Society of Urological Pathology grade group (GG) 2 or greater prostate cancer. \n \nDESIGN, SETTING, AND PARTICIPANTS: This multicenter, prospective randomized clinical trial was conducted in 5 Canadian academic health sciences centers between January 2017 and November 2019, and data were analyzed between January and March 2020. Participants included biopsy-naive men with a clinical suspicion of prostate cancer who were advised to undergo a prostate biopsy. Clinical suspicion was defined as a 5% or greater chance of GG2 or greater prostate cancer using the Prostate Cancer Prevention Trial Risk Calculator, version 2. Additional criteria were serum prostate-specific antigen levels of 20 ng/mL or less (to convert to micrograms per liter, multiply by 1) and no contraindication to MRI. \n \n \n \nINTERVENTIONS: Magnetic resonance imaging–targeted biopsy (MRI-TB) only if a lesion with a Prostate Imaging Reporting and Data System (PI-RADS), v 2.0, score of 3 or greater was identified vs 12-core systematic TRUS biopsy. \n \n \nMAIN OUTCOMES AND MEASURES: The proportion of men with a diagnosis of GG2 or greater cancer. Secondary outcomes included the proportion who received a diagnosis of GG1 prostate cancer; GG3 or greater cancer; no significant cancer but subsequent positive MRI results and/or GG2 or greater cancer detected on a repeated biopsy by 2 years; and adverse events. \n \nRESULTS: \n The intention-to-treat population comprised 453 patients (367 [81.0%] White, 19 [4.2%] African Canadian, 32 [7.1%] Asian, and 10 [2.2%] Hispanic) who were randomized to undergo TRUS biopsy (226 [49.9%]) or MRI-TB (227 [51.1%]), of which 421 (93.0%) were evaluable per protocol. A lesion with a PI-RADS score of 3 or greater was detected in 138 of 221 men (62.4%) who underwent MRI, with 26 (12.1%), 82 (38.1%), and 30 (14.0%) having maximum PI-RADS scores of 3, 4, and 5, respectively. Eighty-three of 221 men who underwent MRI-TB (37%) had a negative MRI result and avoided biopsy. Cancers GG2 and greater were identified in 67 of 225 men (30%) who underwent TRUS biopsy vs 79 of 227 (35%) allocated to MRI-TB (absolute difference, 5%, 97.5% 1-sided CI, −3.4% to ∞; noninferiority margin, −5%). Adverse events were less common in the MRI-TB arm. Grade group 1 cancer detection was reduced by more than half in the MRI arm (from 22% to 10%; risk difference, −11.6%; 95% CI, −18.2% to −4.9%). \n \nCONCLUSIONS AND RELEVANCE: Magnetic resonance imaging followed by selected targeted biopsy is noninferior to initial systematic biopsy in men at risk for prostate cancer in detecting GG2 or greater cancers. \n \nTRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02936258

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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 designRandomized trial
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

Citations24
Published2021
Admission routes1
Has abstractyes

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