Evidence map›Paper›PMID 36743400›Full record

ArticleEuropean urology open science2023

Side-specific, Microultrasound-based Nomogram for the Prediction of Extracapsular Extension in Prostate Cancer.

Adriana M Pedraza, Sneha Parekh, Himanshu Joshi, Ralph Grauer, Vinayak Wagaskar, Laura Zuluaga, Raghav Gupta, Flora Barthe, Jordan Nasri, Krunal Pandav and 4 more

Open access · goldAbstract read
In one paragraph

Article in European urology open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.4field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Adriana M PedrazaDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Sneha ParekhDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Himanshu JoshiDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Ralph GrauerDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Vinayak WagaskarDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Laura ZuluagaDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Raghav GuptaDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Flora BartheDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Jordan NasriDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Krunal PandavDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Dhruti PatelDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Michael A GorinDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Mani MenonDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Ashutosh K TewariDepartment of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Icahn School of Medicine at Mount Sinai · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Prediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario. Objective: To create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing. Design setting and participants: Prospective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis. Outcome measurements and statistical analysis: ECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility. Results and limitations: The areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement. Conclusions: We developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results. Patient summary: The integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

Indexed as

Extracapsular extensionMicroultrasoundMultiparametric magnetic resonance imagingProstate cancer

Identifiers

PMID36743400
PMCPMC9895764
OpenAlexW4313420343

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.