Evidence map›Paper›PMID 37889265›Full record

ReviewAbdominal radiology (New York)2024

Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application.

M Álvaro Berbís, Félix Paulano Godino, Júlia Rodríguez-Comas, Enrique Nava, Roberto García-Figueiras, Sandra Baleato-González, Antonio Luna

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
6.4field-weighted citation impact, top 3% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Radiomics-Based Machine Learning for DeterminingTechnology in cancer research & treatment
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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 3 institutions in 2 countries.

M Álvaro BerbísDepartment of Radiology, HT Médica, San Juan de Dios Hospital, 14960, Córdoba, Spain. a.berbis@htime.org.ORCID 0000-0002-0331-7762
Félix Paulano GodinoDepartment of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain.
Júlia Rodríguez-ComasScientific and Technical Department, Sycai Technologies, 08018, Barcelona, Spain.
Enrique NavaDepartment of Communications Engineering, University of Málaga, 29016, Málaga, Spain.
Roberto García-FigueirasAbdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain.
Sandra Baleato-GonzálezAbdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain.
Antonio LunaDepartment of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain.
Hospital Universitario Virgen de las Nieves · ESHospital San Juan de Dios · CLUniversidad de Málaga · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsPancreatic NeoplasmsHumansMagnetic Resonance ImagingPancreasRadiomicsRetrospective StudiesTomography, X-Ray ComputedComputerized tomographyDeep learningLiverMagnetic resonance imagingPancreasRadiomics

Identifiers

PMID37889265
OpenAlexW4387966507

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.