Evidence map›Paper›PMID 32785796›Full record

ReviewInsights into imaging2020

Radiomics in medical imaging-"how-to" guide and critical reflection.

Janita E van Timmeren, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, Bettina Baessler

2 registry-linked trialsAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Insights into imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 776 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
776citing papers in PubMed, 10 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.

NCT07744555 naactive not recruitingnot on this mapstarted 2026, after this paper: background citation

Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System

TypeinterventionalSponsorUniversidad de la SabanaRan2026 to 2026Enrolled50ConditionsPneumonia, RadiographyArmsArtificial intelligence expert system
NCT07783126 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Deep-learning Prediction of Abdominal Aortic Aneurysm Enlargement in Type II Endoleak After EVAR: Development and Multicenter Validation of an End-to-end Deep-learning Model (the RADAR Study)

TypeobservationalSponsorEnte Ospedaliero Cantonale, BellinzonaRan2026 to 2028Enrolled1,250ConditionsType II Endoleak, Abdominal Aortic Aneurysm EnlargementArmsArtificial intelligence model analyzing preoperative CT angiography
3 · Its place in the literature

Who cites it

776 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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716 more citing papers are in PubMed but not listed here.

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

5 authors.

Janita E van TimmerenDepartment of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Davide CesterInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Stephanie Tanadini-LangDepartment of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Hatem AlkadhiInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Bettina BaesslerInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. Bettina.Baessler@usz.ch.ORCID http://orcid.org/0000-0002-3244-3864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.

Indexed as

Machine learningQuantitative imaging biomarkersRadiomicsRobustnessStandardization

Identifiers

PMID32785796
PMCPMC7423816

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.