Evidence map›Paper›PMID 22083309›Full record

ArticleDer Radiologe2011

[New ultrasound technologies for the diagnostics of prostate cancer].

T de Zordo, M Ladurner, W Horninger, S W Heijmink, W Jaschke, F Aigner, F Frauscher

Abstract read
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In one paragraph

Article in Der Radiologe, 2011. 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
0.7field-weighted citation impact, top 20% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
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 3 institutions in 2 countries.

T de ZordoDepartment für Radiologie, Medizinische Universität Innsbruck, Anichstr. 35, A-6020, Innsbruck, Österreich.
M Ladurner
W Horninger
S W Heijmink
W Jaschke
F Aigner
F Frauscher
Innsbruck Medical University · ATRadboud University Nijmegen · NLUniversität Innsbruck · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

clinical/methodological issueProstate cancer is the most common cancer in men. The diagnosis is based on prostate-specific antigen (PSA), digital rectal examination (DRE) and transrectal ultrasound (TRUS) guided biopsy. These techniques have considerable limitations, which result in unnecessary biopsies. Furthermore the biopsies are associated with morbidity and costs. STANDARD RADIOLOGICAL

methodsStandard gray-scale ultrasound has a low sensitivity and specificity for prostate cancer detection. METHODOLOGICAL INNOVATIONS: New ultrasound technologies, including color- and power Doppler ultrasound, contrast enhanced US and real-time sonoelastography have shown to improve prostate cancer diagnosis. PERFORMANCE: Contrast-enhanced ultrasound has shown a sensitivity of 100% (95% CI, 95%), a negative predictive value (NPV) of 99.8% and a positive predictive value (PPV) of 88.8% for prostate cancer detection. Real-time sonoelastography has shown a sensitivity of 86%, a specificity of 81% and NPV of 91% for prostate cancer diagnosis. ACHIEVEMENTS: Most studies show that these new ultrasound modalities demonstrate a 1.5 to 2.5 times higher detection of prostate cancer per biopsy specimen compared with systematic biopsy. Multicenter studies results are at present lacking but are, however ongoing. PRACTICAL RECOMMENDATIONS: In patients with suspected prostate cancer (elevated PSA, suspicious DRE) these new ultrasound techniques should be used. These techniques can detect prostate cancer and allow a targeted biopsy approach.

Indexed as

HumansImage EnhancementMaleProstatic NeoplasmsUltrasonography

Identifiers

PMID22083309
OpenAlexW10010987

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.