Evidence map›Paper›PMID 41117971›Full record

ArticleWorld journal of urology2025

Evaluation of inflammatory markers in survival analysis of patients undergoing radical cystectomy using machine learning.

Naci Burak Çınar, Hasan Yılmaz, Efe Yılmaz Taşyürek, Meltem Kurt Pehlivanoğlu, Sevinç İlhan Omurca, Muhlis Ünal, Kerem Teke

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Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Naci Burak Çınar *Department of Urology, Kutahya City Hospital, Kutahya, Turkey.ORCID http://orcid.org/0000-0002-2199-8370
Hasan Yılmaz *Department of Urology, Kocaeli University School of Medicine, Umuttepe Campus, 41380, Kocaeli, Turkey. hasan.yilmazmd@kocaeli.edu.tr.ORCID http://orcid.org/0000-0003-2512-8820
Efe Yılmaz Taşyürek *Faculty of Engineering, Department of Computer Engineering, Kocaeli University, Kocaeli, Turkey.ORCID http://orcid.org/0009-0002-4049-9566
Meltem Kurt Pehlivanoğlu *Faculty of Engineering, Department of Computer Engineering, Kocaeli University, Kocaeli, Turkey.ORCID http://orcid.org/0000-0002-7581-9390
Sevinç İlhan Omurca *Faculty of Engineering, Department of Computer Engineering, Kocaeli University, Kocaeli, Turkey.ORCID http://orcid.org/0000-0003-1214-9235
Muhlis ÜnalDepartment of Urology, Kocaeli University School of Medicine, Umuttepe Campus, 41380, Kocaeli, Turkey.ORCID http://orcid.org/0000-0002-9361-8859
Kerem Teke *Department of Urology, Kocaeli University School of Medicine, Umuttepe Campus, 41380, Kocaeli, Turkey.ORCID http://orcid.org/0000-0001-9030-4662

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to create a Machine learning (ML) model using patient demographic, clinical and pathological data for prediction of overall survival in patients treated with radical cystectomy (RC). Secondly, we evaluated whether inflammatory markers contributed to this model.

methodsWe conducted a retrospective analysis of the institutional cystectomy database and identified consecutive RC patients. Dataset-1 (DS-1) was analyzed in ML models using 30 original features (including the target feature) encompassing preoperative, intraoperative, and postoperative data of the patients. All derived inflammatory markers were cumulatively added to DS-1 to create DS-2, and to test the specific contribution of inflammatory markers, they were systematically integrated in an ordinary order based on their predictive ability (DS-3). Markers without predictive contribution were excluded from the DS-3 model. In addition, the Shapley Additive Explanations (SHAP) method was used to examine the importance of each clinical feature and inflammatory marker.

resultsThe median age of the 241 patients was 65 years. The mortality rate was 60.2% (145/241). Two- and five-year overall survival (OS) rates were 54.7% and 37.2%, respectively. According to DS-1, F

conclusionsML models derived using demographic/clinical features resulted in a maximum F

Indexed as

CystectomyMachine LearningUrinary Bladder NeoplasmsAgedBiomarkersFemaleHumansInflammationMaleMiddle AgedRetrospective StudiesSurvival AnalysisSurvival RateBiomarkersArtificial intelligenceInflammatory markersMachine learningRadical cystectomyShapley additive explanations (SHAP)Survival

Identifiers

PMID41117971

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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.