Evidence map›Paper›PMID 32060219›Full record

ReviewJournal of nuclear medicine : official publication, Society of Nuclear Medicine2020

Introduction to Radiomics.

Marius E Mayerhoefer, Andrzej Materka, Georg Langs, Ida Häggström, Piotr Szczypiński, Peter Gibbs, Gary Cook

Registry-linked trialAbstract readReview
In one paragraph

Review in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06579768 (Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images), which is not on this map. Cited by 1,074 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,074citing papers in PubMed, 7 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.

NCT06579768 recruitingnot on this mapstarted 2024, after this paper: background citation

Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study

TypeobservationalSponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityRan2024 to 2026Enrolled300ConditionsJawbone Cysitc LesionArmsdifferent types of computed tomography (CT) scans
3 · Its place in the literature

Who cites it

1,074 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
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  7. Role of Radiomics to Predict Disease Recurrence in Sinonasal Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2026
    Pooled it
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  12. Digital twin validation and predictive value of delta radiomics based on tabular transformer for synergistic muscle imbalance in lumbar disc degeneration.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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1,014 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

7 authors.

Marius E MayerhoeferDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York mayerhom@mskcc.org.
Andrzej MaterkaInstitute of Electronics, Lodz University of Technology, Lodz, Poland.
Georg LangsDepartment of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Ida HäggströmDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Piotr SzczypińskiInstitute of Electronics, Lodz University of Technology, Lodz, Poland.
Peter GibbsDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
Gary CookCancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; and.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

Indexed as

Image Processing, Computer-AssistedHumansNuclear Medicineartificial intelligencemachine learningPETradiomicssingle-photon emission tomography

Identifiers

PMID32060219
PMCPMC9374044

What OpenQuestion holds

Textmetadata
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.