MP81-10 RISK STRATIFICATION FOR EQUIVOCAL PI-RADS 3 RESULTS: CAN MICRO-ULTRASOUND HELP DETERMINE WHICH MEN TO BIOPSY?
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening VIII (MP81)1 Apr 2020MP81-10 RISK STRATIFICATION FOR EQUIVOCAL PI-RADS 3 RESULTS: CAN MICRO-ULTRASOUND HELP DETERMINE WHICH MEN TO BIOPSY? Georg Salomon*, Giovanni Lughezzani, Hannes Cash, Laura Wiemer, Robin Heckmann, Sebastian Hofbauer, Ander Astobieta, Andrea Sánchez, Frédéric Staerman, Laurent Lopez, Richard Gaston, Thierry Piéchaud, Gregg Eure, Eric Klein, Robert Abouassaly, and Sangeet Ghai Georg Salomon*Georg Salomon* More articles by this author , Giovanni LughezzaniGiovanni Lughezzani More articles by this author , Hannes CashHannes Cash More articles by this author , Laura WiemerLaura Wiemer More articles by this author , Robin HeckmannRobin Heckmann More articles by this author , Sebastian HofbauerSebastian Hofbauer More articles by this author , Ander AstobietaAnder Astobieta More articles by this author , Andrea SánchezAndrea Sánchez More articles by this author , Frédéric StaermanFrédéric Staerman More articles by this author , Laurent LopezLaurent Lopez More articles by this author , Richard GastonRichard Gaston More articles by this author , Thierry PiéchaudThierry Piéchaud More articles by this author , Gregg EureGregg Eure More articles by this author , Eric KleinEric Klein More articles by this author , Robert AbouassalyRobert Abouassaly More articles by this author , and Sangeet GhaiSangeet Ghai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000973.010AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Reducing unnecessary prostate biopsy procedures is an important clinical goal to reduce pain and anxiety for the patient, as well as the risk of infection and overtreatment. Multiparametric (mpMRI) has been proposed as an effective strategy to reduce the need for a prostate biopsy both in the initial and in the repeat biopsy setting. However, indeterminate or equivocal findings at mpMRI can pose a diagnostic challenge. We aimed to determine whether micro-ultrasound could help to further stratify the need for a prostate biopsy in patients with PIRADS 3 lesion at mpMRI. METHODS: This study was based on a retrospective series of patients presenting with at least one PI-RADS 3 lesion at mpMRI at one of 7 international sites. All patients were imaged with micro-ultrasound using the ExactVu™ (Exact Imaging, Markham, Canada) system, and the presence of suspicious lesions was determined and graded according to the PRI-MUS™ (Prostate risk identification using micro-ultrasound)1 protocol. Maximum PRI-MUS score for each subject was used to determine whether the case was non-suspicious on micro-ultrasound (PRI-MUS 1 or 2), equivocal (PRI-MUS 3), or suspicious (PRI-MUS 4 or 5). All patients with a suspicious (PRI-MUS >2) lesion were subjected to a micro-ultrasound guided targeted biopsy. In addition, mpMRI targeted biopsies on PI-RADS 3 lesions were also obtained either with a cognitive or with a fusion biopsy technique according to each center protocol. RESULTS: 144 subjects were included. Overall prostate cancer detection rate for PI-RADS 3 subjects was 48% (69/144), while 20% (29/144) of patients were diagnosed with a clinically significant prostate cancer defined as a ISUP Grade Group (GG) > 1 tumor. PRI-MUS was able to provide significant risk stratification in this population, with non-suspicious micro-ultrasound imaging reducing the risk of finding GG>1 cancer by more than half to 5% (1/19). Equivocal micro-ultrasound provided little additional information with a GG>1 detection rate of 14% (5/35), while suspicious micro-ultrasound imaging resulted in a significant 17% relative increase in GG>1 detection rate to 26% (29/90, p=0.02). CONCLUSIONS: Micro-ultrasound imaging and PRI-MUS protocol findings appear to provide useful additional information in the case of equivocal mpMRI results. When combined with other clinical risk indicators such as PSA, PSA density and family history, it may be possible to better advise patients on the necessity of a biopsy using this data. Source of Funding: None. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1240-e1240 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Georg Salomon* More articles by this author Giovanni Lughezzani More articles by this author Hannes Cash More articles by this author Laura Wiemer More articles by this author Robin Heckmann More articles by this author Sebastian Hofbauer More articles by this author Ander Astobieta More articles by this author Andrea Sánchez More articles by this author Frédéric Staerman More articles by this author Laurent Lopez More articles by this author Richard Gaston More articles by this author Thierry Piéchaud More articles by this author Gregg Eure More articles by this author Eric Klein More articles by this author Robert Abouassaly More articles by this author Sangeet Ghai 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".