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PD23-03 HIGH RESOLUTION 29 MHZ MICRO-ULTRASOUND IN THE DIAGNOSIS OF PRIMARY AND RECURRENT PROSTATE CANCER

2019· article· en· W2940620345 on OpenAlexaboutno aff
Laurence Klotz, Dixon Woon

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerRadiologyBiopsyProstateUltrasoundProstate biopsyCancerNuclear medicineInternal medicine

Abstract

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You have accessJournal of UrologyImaging/Radiology: Uroradiology I (PD23)1 Apr 2019PD23-03 HIGH RESOLUTION 29 MHZ MICRO-ULTRASOUND IN THE DIAGNOSIS OF PRIMARY AND RECURRENT PROSTATE CANCER Laurence Klotz* and Dixon Woon Laurence Klotz*Laurence Klotz* More articles by this author and Dixon WoonDixon Woon More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555771.27764.d2AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: High resolution micro-ultrasound (micro-US) is a novel technology which offers the potential to image PCa and perform targeted biopsies with significantly improved cancer visualization compared to conventional ultrasound, potentially obviating the need for MRI. Micro-US incorporates a scoring system (PRIMUS, Grades 1-5) that is similar to the PIRADS-2 system used for MRI. This facilitates assignment of a risk category based on the characteristics of the region of interest. We evaluate the NPV and PPV for each PRIMUS risk category. METHODS: 50 patients underwent prostate biopsy using micro-US (ExactVu micro-US, Exact Imaging, Markham, Canada). 32 were undiagnosed men at risk for PCa, and 18 had been previously treated with focal therapy with HIFU or TULSA and were undergoing post treatment biopsies. In the undiagnosed patients, targeted and 12 core systematic biopsies were performed. 28 had had a prior MRI. Physicians were blinded to the MRI results at the time of biopsy. RESULTS: The NPV for PRIMUS scores <= 3 for clinically significant PCa was 96% (27/29). Of 19 cases with a PRIMUS 4 or 5 lesion, 63% had a positive biopsy. In 10 cases with GG ≥2, 9 (90%) had a PRI-MUS ≥3 lesion at the cancer location. The single GG ≥ 2 cancer missed on micro-US was a post-focal therapy case whose MRI was also negative. Of 28 cases who had both MRI and micro-US, 12 had a positive biopsy. 7/12 had targets seen on both modalities. 1/12 was negative on both; 4/12 had GG 2 with a positive micro-US and negative MRI. All patients with a PIRADS score 4-5 had a PRIMUS 4-5 lesion on micro-US at the same location. The PPV for each PRIMUS score for all PCa grades was 1: 0%, 2: 33%, 3: 29%, 4: 63%, 5: 67%, and for GG 2, 1: 0%, 2: 0%, 3: 4%, 4:38%, 5: 67%. CONCLUSIONS: High resolution micro-ultrasound with targeted biopsy appears to offer comparable sensitivity, specificity, PPV and NPV to MP-MRI, in the setting of both primary diagnosis and post-treatment recurrence. Micro-ultrasound-based biopsies combine imaging and targeting in a single session and involves substantially less cost and complexity than MRI combined with fusion targeting. Source of Funding: None. The Exact Vue system was provided by Exact Imaging, Markham Ontario Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e393-e394 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Laurence Klotz* More articles by this author Dixon Woon More articles by this author Expand All Advertisement PDF downloadLoading ...

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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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0170.006

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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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