MP13-19 COMPARISON OF CANCER DETECTION RATES IN MICRO-ULTRASOUND BIOPSIES VERSUS ROBOTIC ULTRASOUND-MAGNETIC RESONANCE IMAGING FUSION BIOPSIES FOR PROSTATE CANCER
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening I (MP13)1 Apr 2019MP13-19 COMPARISON OF CANCER DETECTION RATES IN MICRO-ULTRASOUND BIOPSIES VERSUS ROBOTIC ULTRASOUND-MAGNETIC RESONANCE IMAGING FUSION BIOPSIES FOR PROSTATE CANCER Oliver R. Claros*, Fabio Muttin, Rafael R. Tourinho-Barbosa, Anna C. Gallardo, Eric Barret, François Rozet, Nathalie Cathala, Dominique Prapotnich, Annick Mombet, Rafael Sanchez-Salas, and Xavier Cathelineau Oliver R. Claros*Oliver R. Claros* More articles by this author , Fabio MuttinFabio Muttin More articles by this author , Rafael R. Tourinho-BarbosaRafael R. Tourinho-Barbosa More articles by this author , Anna C. GallardoAnna C. Gallardo More articles by this author , Eric BarretEric Barret More articles by this author , François RozetFrançois Rozet More articles by this author , Nathalie CathalaNathalie Cathala More articles by this author , Dominique PrapotnichDominique Prapotnich More articles by this author , Annick MombetAnnick Mombet More articles by this author , Rafael Sanchez-SalasRafael Sanchez-Salas More articles by this author , and Xavier CathelineauXavier Cathelineau More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555295.45193.66AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: We aimed to compare the cancer detection rates in patients who underwent micro-ultrasound biopsy (MB) versus Robotic ultrasound-magnetic resonance imaging fusion biopsies (RFB) for prostate cancer. METHODS: Between February 2017 and September 2018, 451 biopsies were performed at our institution. We performed a matched pair analysis based on prostate volume and PSA. We selected 271 patients that underwent target biopsy that composed the population of the study. In total 223 men underwent RFB, and 48 underwent MB. The study cohort was divided into two groups: robotic ultrasound-magnetic resonance imaging fusion biopsy (Group A) and micro-ultrasound biopsy (Group B). Micro-ultrasound imaging was performed using the high resolution ExactVu system (29 MHz, Exact Imaging, Markham, Canada). RFB was performed using Artemis Device (Eigen, Grass Valley, CA). Biopsy samples were taken from targets in each modality, plus systematic samples. RESULTS: There were no differences according cancer detection rates except for target detection rates of clinically significant tumors. The prostate cancer detection rate was 67.7% (151) in group A and 62.5% (30) in group B (p=0.48) The detection of clinically significant cancer defined as patients with Gleason score greater or equal to 3+ 4 was 31.8%(71) in group A and 39.5% (19) in group B (p=0.31). The cancer detection rate of random biopsies were similar in group A and group B (21.5% vs. 22.91% respectively ; p=0.83). Patients from Group B had higher clinically significant tumours detection in target biopsies (37.5% vs. 22.86%; p=0.035). CONCLUSIONS: Our study suggests that micro-ultrasound biopsy may be comparable to RFB according to prostate cancer detection. Micro-ultrasound might play a role in cognitive fusion biopsies. Source of Funding: none Paris, France© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e184-e185 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Oliver R. Claros* More articles by this author Fabio Muttin More articles by this author Rafael R. Tourinho-Barbosa More articles by this author Anna C. Gallardo More articles by this author Eric Barret More articles by this author François Rozet More articles by this author Nathalie Cathala More articles by this author Dominique Prapotnich More articles by this author Annick Mombet More articles by this author Rafael Sanchez-Salas More articles by this author Xavier Cathelineau More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".