Evidence map›Paper›PMID 23982601›Full record

ArticleBritish journal of cancer2013

Accuracy of the EORTC risk tables and of the CUETO scoring model to predict outcomes in non-muscle-invasive urothelial carcinoma of the bladder.

E Xylinas, M Kent, L Kluth, A Pycha, E Comploj, R S Svatek, Y Lotan, Q-D Trinh, P I Karakiewicz, S Holmang and 4 more

2 registry-linked trialsAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 95 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 10 pooled it
–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.

NCT03174912 phase4completednot on this map

In the Prediction of Recurrence and Progression of NMIBC; EORTC or CUETO or Both?

TypeinterventionalSponsorAnkara Training and Research HospitalRan2007 to 2016Enrolled400ConditionsBladder Cancer, Disease Progression, Recurrent Bladder CancerArmsIntravesical BCG instillation
NCT06565923 active not recruitingnot on this mapstarted 2024, after this paper: background citation

Study on the Staging and Prognosis Model of Bladder Cancer Based on Artificial Intelligence and Multimodal Omics Features

Typeobservational_patient_registrySponsorThe First Affiliated Hospital with Nanjing Medical UniversityRan2024 to 2025Enrolled200ConditionsBladder Cancer
3 · Its place in the literature

Who cites it

95 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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35 more citing papers are in PubMed but not listed here.

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.

E Xylinas1] Department of Urology, Weill Cornell Medical College, New York, NY, USA [2] Department of Urology, Cochin Hospital, APHP, Paris Descartes University, Paris, France.
M Kent
L Kluth
A Pycha
E Comploj
R S Svatek
Y Lotan
Q-D Trinh
P I Karakiewicz
S Holmang
D S Scherr
M Zerbib
A J Vickers
S F Shariat

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

backgroundThe European Organization for Research and Treatment of Cancer (EORTC) risk tables and the Spanish Urological Club for Oncological Treatment (CUETO) scoring model are the two best-established predictive tools to help decision making for patients with non-muscle-invasive bladder cancer (NMIBC). The aim of the current study was to assess the performance of these predictive tools in a large multicentre cohort of NMIBC patients.

methodsWe performed a retrospective analysis of 4689 patients with NMIBC. To evaluate the discrimination of the models, we created Cox proportional hazard regression models for time to disease recurrence and progression. We incorporated the patients calculated risk score as a predictor into both of these models and then calculated their discrimination (concordance indexes). We compared the concordance index of our models with the concordance index reported for the models.

resultsWith a median follow-up of 57 months, 2110 patients experienced disease recurrence and 591 patients experienced disease progression. Both tools exhibited a poor discrimination for disease recurrence and progression (0.597 and 0.662, and 0.523 and 0.616, respectively, for the EORTC and CUETO models). The EORTC tables overestimated the risk of disease recurrence and progression in high-risk patients. The discrimination of the EORTC tables was even lower in the subgroup of patients treated with BCG (0.554 and 0.576 for disease recurrence and progression, respectively). Conversely, the discrimination of the CUETO model increased in BCG-treated patients (0.597 and 0.645 for disease recurrence and progression, respectively). However, both models overestimated the risk of disease progression in high-risk patients.

conclusionThe EORTC risk tables and the CUETO scoring system exhibit a poor discrimination for both disease recurrence and progression in NMIBC patients. These models overestimated the risk of disease recurrence and progression in high-risk patients. These overestimations remained in BCG-treated patients, especially for the EORTC tables. These results underline the need for improving our current predictive tools. However, our study is limited by its retrospective and multi-institutional design.

Indexed as

AgedCohort StudiesDisease ProgressionFemaleHumansMaleMiddle AgedNeoplasm InvasivenessNeoplasm Recurrence, LocalRandomized Controlled Trials as TopicRetrospective StudiesRisk AssessmentRisk FactorsTreatment OutcomeUrinary Bladder NeoplasmsUrothelium

Identifiers

PMID23982601
PMCPMC3776972

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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Registered trials

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.