MP09-09 CORRELATION BETWEEN PROSTATE MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING AND HIGH-RESOLUTION MICRO-ULTRASOUND
Bibliographic record
Abstract
You have accessJournal of UrologyCME1 Apr 2023MP09-09 CORRELATION BETWEEN PROSTATE MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING AND HIGH-RESOLUTION MICRO-ULTRASOUND Nicholas Pickersgill, M. Hassan Alkazemi, Joel Vetter, Adam Ostergar, Nimrod Barashi, Grant Henning, and Arjun Sivaraman Nicholas PickersgillNicholas Pickersgill More articles by this author , M. Hassan AlkazemiM. Hassan Alkazemi More articles by this author , Joel VetterJoel Vetter More articles by this author , Adam OstergarAdam Ostergar More articles by this author , Nimrod BarashiNimrod Barashi More articles by this author , Grant HenningGrant Henning More articles by this author , and Arjun SivaramanArjun Sivaraman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate multiparametric magnetic resonance imaging (pMRI) has emerged as a valuable tool in the diagnostic pathway for prostate cancer. The recent introduction of high-resolution micro-ultrasound (microUS) guided prostate biopsy aims to further improve the detection of clinically significant prostate cancer (CSCaP). The correlation between these two imaging modalities is poorly understood. We investigated correlation in lesion identification between microUS and pMRI. METHODS: We reviewed our prospectively maintained database of 200 consecutive patients who underwent transperineal microUS-guided biopsy with the ExactVu™platform (Exact Imaging, Markham, Canada) between February 2021 and April 2022. The Prostate Risk Identification using MicroUS (PRI-MUS) protocol was utilized to risk stratify prostate lesions, with PRI-MUS 3-5 defined as positive. pMRI lesions were classified according to PI-RADS version 2. Clinicopathologic outcomes were analyzed. Spearman correlation testing was computed to assess the relationship between PRI-MUS and PI-RADS. Patients with sufficient data for analysis were included. RESULTS: A total of 159 patients met inclusion criteria. Of these, 117 were biopsy-naïve, 19 had a prior negative biopsy and 20 were on active surveillance. Mean±standard deviation (SD) age was 66.6±7.7 years and PSA was 10.1±4.4 ng/mL. A total of 112 patients underwent multiparametric magnetic resonance imaging (mpMRI) prior to biopsy, of which 56 were found to have PIRADS 3-5 lesions. There was a weak positive correlation between PRI-MUS and PI-RADS (r=0.23, p=0.013) (Figure 1). CONCLUSIONS: This preliminary comparison between PRI-MUS and PI-RADS scoring demonstrates a weak positive correlation between the two modalities. This may be attributable to a significant number of patients with negative pMRI who were found to have PRI-MUS 3-5 lesions. Given the rising utilization of micro-US-guided prostate biopsy and widespread use of pMRI, further prospective studies are needed to compare their ability to detect clinically significant prostate cancer. Source of Funding: Midwest Stone Institute © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e107 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nicholas Pickersgill More articles by this author M. Hassan Alkazemi More articles by this author Joel Vetter More articles by this author Adam Ostergar More articles by this author Nimrod Barashi More articles by this author Grant Henning More articles by this author Arjun Sivaraman More articles by this author Expand All Advertisement PDF downloadLoading ...
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.254 | 0.066 |
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".