PD09-11 POTENTIAL ROLE OF MICRO-ULTRASOUND IN THE DIFFERENTIATION BETWEEN MUSCLE-INVASIVE AND NON-MUSCLE INVASIVE BLADDER CANCER: A PROSPECTIVE ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Non-invasive I (PD09)1 Sep 2021PD09-11 POTENTIAL ROLE OF MICRO-ULTRASOUND IN THE DIFFERENTIATION BETWEEN MUSCLE-INVASIVE AND NON-MUSCLE INVASIVE BLADDER CANCER: A PROSPECTIVE ANALYSIS Pietro Diana, Giovanni Lughezzani, Alessandro Uleri, Nicola Frego, Roberto Contieri, Marco Paciotti, Vittorio Fasulo, Piergiuseppe Colombo, Nicolomaria Buffi, Alberto Saita, Paolo Casale, Rodolfo Hurle, and Massimo Lazzeri Pietro DianaPietro Diana More articles by this author , Giovanni LughezzaniGiovanni Lughezzani More articles by this author , Alessandro UleriAlessandro Uleri More articles by this author , Nicola FregoNicola Frego More articles by this author , Roberto ContieriRoberto Contieri More articles by this author , Marco PaciottiMarco Paciotti More articles by this author , Vittorio FasuloVittorio Fasulo More articles by this author , Piergiuseppe ColomboPiergiuseppe Colombo More articles by this author , Nicolomaria BuffiNicolomaria Buffi More articles by this author , Alberto SaitaAlberto Saita More articles by this author , Paolo CasalePaolo Casale More articles by this author , Rodolfo HurleRodolfo Hurle More articles by this author , and Massimo LazzeriMassimo Lazzeri More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001977.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Magnetic resonance imaging (MRI) has been proposed as a staging tool for bladder cancer (BC), but its use has been limited by its high costs and limited availability. Microultrasound (mUS) is a novel technology capable providing high-resolution images, which was initially developed for the diagnosis of PCa. In the setting of BC, a preliminary study has showed that mUS may be capable to differentiate the bladder wall layers, identify BC and differentiate between non-muscle invasive (NMIBC) and muscle-invasive BC (MIBC). The aim of this study is to confirm on a larger scale the potential role of mUS in the identification and characterization of BC. METHODS: This is an observational study of prospectively collected data performed in patients diagnosed with primary BC and scheduled for an endoscopic treatment. Micro-US was performed before transurethral resection (TUR) of bladder tumor using the ExactVu system with an EV29L 29-MHz side-fire transducer (Exact Imaging, Markham, Canada). The endpoints were to assess the diagnostic accuracy of mUS in identifying BC and in differentiating between NMIBC and MIBC comparing the mUS findings with the histopathological results. RESULTS: Between April 2018 and December 2020, we prospectively enrolled 94 consecutive patients with primary BC. Micro-US window was not acceptable in 10 patients and 2 patient presented with a tumor looking mass at both cystoscopy and abdominal US but histology was negative for bladder cancer showing chronic cystitis. In all the other 82 patients, mUS clearly identified the three bladder wall layers and the BC as heterogeneous structures protruding from the normal wall. The reported accuracy, sensitivity, specificity, and positive, and negative predictive values for mUS were 82.9%, 87.5%, 81%, 65.6%, and 94%, respectively. The ROC curve analysis for the mUS in detecting MIBC shows an AUC of 0.843 (CI 95% 0.746-0.940). CONCLUSIONS: These results confirm in a larger population the potential role of mUS to differentiate the bladder wall layers and reliably differentiate between NMIBC and MIBC in the great majority of patients. In consequence, mUS may represent a cheaper and less time-consuming alternative to mpMRI. Further comparative studies between mUS and MRI are still warranted to validate this imaging technique in the BC staging before its introduction in clinical practice. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e124-e124 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Pietro Diana More articles by this author Giovanni Lughezzani More articles by this author Alessandro Uleri More articles by this author Nicola Frego More articles by this author Roberto Contieri More articles by this author Marco Paciotti More articles by this author Vittorio Fasulo More articles by this author Piergiuseppe Colombo More articles by this author Nicolomaria Buffi More articles by this author Alberto Saita More articles by this author Paolo Casale More articles by this author Rodolfo Hurle More articles by this author Massimo Lazzeri More articles by this author Expand All Advertisement Loading ...
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".