Evidence map›Paper›PMID 25882557›Full record

ReviewThe Urologic clinics of North America2015

Advances in imaging technologies in the evaluation of high-grade bladder cancer.

Dimitar V Zlatev, Emanuela Altobelli, Joseph C Liao

Abstract readReview
In one paragraph

Review in The Urologic clinics of North America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 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.

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

25 citing papers in PubMed, 1 synthesis or guideline 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

3 authors.

Dimitar V ZlatevDepartment of Urology, Stanford University School of Medicine, 300 Pasteur Drive, Room S-287, Stanford, CA 94305-5118, USA.
Emanuela AltobelliDepartment of Urology, Stanford University School of Medicine, 300 Pasteur Drive, Room S-287, Stanford, CA 94305-5118, USA; Department of Urology, Campus Biomedico, Via Alvaro del Portillo 200, Rome 00128, Italy.
Joseph C LiaoDepartment of Urology, Stanford University School of Medicine, 300 Pasteur Drive, Room S-287, Stanford, CA 94305-5118, USA; Urology Section, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304, USA. Electronic address: jliao@stanford.edu.

Funding

Optical Imaging of Bladder Cancer with Molecular Contrast AgentsR01CA160986 · NCI · STANFORD UNIVERSITY · PI LIAO, JOSEPH C · 2011 to 2014
$1.7M
NCI NIH HHS R01 CA160986
6 · The paper itself

Abstract

Bladder cancer ranges from a low-grade variant to high-grade disease. Assessment for treatment depends on white light cystoscopy, however because of its limitations there is a need for improved visualization of flat, multifocal, high-grade, and muscle-invasive lesions. Photodynamic diagnosis and narrow-band imaging provide additional contrast enhancement of bladder tumors and have been shown to improve detection rates. Confocal laser endomicroscopy and optical coherence tomography enable real-time, high-resolution, subsurface tissue characterization with spatial resolutions similar to histology. Molecular imaging offers the potential for the combination of optical imaging technologies with cancer-specific molecular agents to improve the specificity of disease detection.

Indexed as

CystoscopyMicroscopy, ConfocalMolecular ImagingNarrow Band ImagingTomography, Optical CoherenceHumansMultimodal ImagingNeoplasm GradingUrinary Bladder NeoplasmsBladder cancerConfocal laser endomicroscopyFluorescence cystoscopyMolecular imagingNarrow band imagingOptical coherence tomographyPhotodynamic diagnosis

Identifiers

PMID25882557
PMCPMC4402158

What OpenQuestion holds

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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.