Evidence map›Paper›PMID 27785429›Full record

ReviewTranslational andrology and urology2016

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

Aurélie Mbeutcha, Romain Mathieu, Morgan Rouprêt, Kilian M Gust, Alberto Briganti, Pierre I Karakiewicz, Shahrokh F Shariat

Open access · hybridAbstract readReview
In one paragraph

Review in Translational andrology and urology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 14% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

  1. Pooled it
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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

7 authors at 7 institutions in 5 countries.

Aurélie MbeutchaDepartment of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna General Hospital, Vienna, Austria; ; Department of Urology, University of Nice Sophia-Antipolis, Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, Nice, France.
Romain MathieuDepartment of Urology, Rennes University Hospital, Rennes, France.
Morgan RouprêtDepartment of Urology, Pitié-Salpêtrière, APHP, University Paris VI, Paris, France.
Kilian M GustDepartment of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
Alberto BrigantiUnit of Urology, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Ospedale S. Raffaele, Milan, Italy.
Pierre I KarakiewiczDepartment of Urology, University of Montreal, Montreal, QC, Canada.
Shahrokh F ShariatDepartment of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna General Hospital, Vienna, Austria; ; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA; ; Department of Urology, Weill Cornell Medical College, New York, NY, USA.
Centre Hospitalier Universitaire de Rennes · FRHôpital Pasteur · FRIRCCS Ospedale San Raffaele · ITMedical University of Vienna · ATPitié-Salpêtrière Hospital · FRThe University of Texas Southwestern Medical Center · USUniversité de Montréal · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

Indexed as

biomarkersdisease recurrencenomogramspredictive toolsprognosisprognostic factorsrisk stratificationsurvivalUpper tract urothelial carcinoma (UTUC)

Identifiers

PMID27785429
PMCPMC5071205
OpenAlexW2530993582

What OpenQuestion holds

Textmetadata
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.