Evidence map›Paper›PMID 39497708›Full record

ArticleFrontiers in oncology2024

Deciphering the molecular heterogeneity of intermediate- and (very-)high-risk non-muscle-invasive bladder cancer using multi-layered

Murat Akand, Tatjana Jatsenko, Tim Muilwijk, Thomas Gevaert, Steven Joniau, Frank Van der Aa

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Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
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

6 authors.

Murat AkandDepartment of Urology, University Hospitals Leuven, Leuven, Belgium.
Tatjana JatsenkoLaboratory for Cytogenetics and Genome Research, KU Leuven, Leuven, Belgium.
Tim MuilwijkDepartment of Urology, University Hospitals Leuven, Leuven, Belgium.
Thomas GevaertDepartment of Pathology, AZ Klina, Brasschaat, Belgium.
Steven JoniauDepartment of Urology, University Hospitals Leuven, Leuven, Belgium.
Frank Van der AaDepartment of Urology, University Hospitals Leuven, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered

Indexed as

bladder cancerepigeneticsgenomicsnon-muscle-invasive-omicsprogressionrecurrencetranscriptomics

Identifiers

PMID39497708
PMCPMC11532112

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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.