Evidence map›Paper›PMID 31231702›Full record

ReviewTranslational gastroenterology and hepatology2019

Pancreatic cancer detection and characterization-state of the art cross-sectional imaging and imaging data analysis.

Georgios Kaissis, Rickmer Braren

Open access · diamondAbstract readReview
In one paragraph

Review in Translational gastroenterology and hepatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 13% 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, 22 citations in OpenAlex.

  1. Article
  2. Radiomics in surgical oncology: applications and challenges.Computer assisted surgery (Abingdon, England) · 2021
    Review
  3. Article
  4. Review
  5. Article
  6. Article
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  8. Review
  9. 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

2 authors at 1 institution in 1 country.

Georgios KaissisInstitute of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Translational Oncology and Quantitative Imaging/Data Science Laboratory, Munich, Germany.
Rickmer BrarenInstitute of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Translational Oncology and Quantitative Imaging/Data Science Laboratory, Munich, Germany.
Technical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) represents a deadly disease, prognosticated to become the 2nd most common cause of cancer related death in the western world by 2030. State of the art radiologic high-resolution cross-sectional imaging by computed tomography (CT) and magnetic resonance imaging (MRI) represent advanced techniques for early lesion detection, pre-therapeutic patient staging and therapy response monitoring. In light of molecular taxonomies currently under development, the implementation of advanced imaging data post-processing pipelines and the integration of imaging and clinical data for the development of risk assessment and clinical decision support tools are required. This review will present the current state of cross-sectional radiologic imaging and image post-processing related to PDAC.

Indexed as

computed tomography (CT)magnetic resonance imaging (MRI)Pancreatic cancer

Identifiers

PMID31231702
PMCPMC6556687
OpenAlexW2946673319

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.