Evidence map›Paper›PMID 33447580›Full record

ReviewGland surgery2020

New advances in CT imaging of pancreas diseases: a narrative review.

Andrea Agostini, Alessandra Borgheresi, Federico Bruno, Raffaele Natella, Chiara Floridi, Marina Carotti, Andrea Giovagnoni

Abstract readReview
In one paragraph

Review in Gland surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. 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.

Andrea AgostiniDepartment of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona (AN), Italy.
Alessandra BorgheresiDepartment of Radiology, University Hospital "Umberto I - Lancisi - Salesi", Ancona (AN), Italy.
Federico BrunoDepartment of Biotechnological and Applied Sciences, University of L'Aquila, L'Aquila, Italy.
Raffaele NatellaDepartment of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy.
Chiara FloridiDepartment of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona (AN), Italy.
Marina CarottiDepartment of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona (AN), Italy.
Andrea GiovagnoniDepartment of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona (AN), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Indexed as

CT quantitativedual energy CTPancreasperfusion CTtexture analysis

Identifiers

PMID33447580
PMCPMC7804533

What Socratic 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 Socratic graph.