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26-06-2023 | Editorial

Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

Authors: Burak Kocak, Leonid L. Chepelev, Linda C. Chu, Renato Cuocolo, Brendan S. Kelly, Philipp Seeböck, Yee Liang Thian, Robbert W. van Hamersvelt, Alan Wang, Stuart Williams, Jan Witowski, Zhongyi Zhang, Daniel Pinto dos Santos

Published in: European Radiology | Issue 11/2023

Excerpt

A simple PubMed search using the term “radiomics” on February 28, 2023, reveals 7857 publications at the time of the writing, with no time filter applied. Despite this level of research activity, there is a substantial gap between the number of radiomics-based publications and their actual clinical use [1]. A typical radiomics workflow consists of numerous steps, each of which may be influenced by a variety of factors [2]. This ultimately leads to variability that has a significant impact on reproducibility, and in turn clinical translation, resulting in the well-known “reproducibility crisis” in radiomics [3]. …

Pinto Dos Santos D, Dietzel M, Baessler B (2021) A decade of radiomics research: are images really data or just patterns in the noise? Eur Radiol 31:1–4. https://doi.org/10.1007/s00330-020-07108-wCrossRefPubMed

Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö (2019) Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol 25:485–495. https://doi.org/10.5152/dir.2019.19321CrossRefPubMedPubMedCentral

Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46:2638–2655. https://doi.org/10.1007/s00259-019-04391-8CrossRefPubMed

Vallières M, Zwanenburg A, Badic B, Le Rest CC, Visvikis D, Hatt M (2018) Responsible radiomics research for faster clinical translation. J Nucl Med 59:189–193. https://doi.org/10.2967/jnumed.117.200501

Hatt M, Krizsan AK, Rahmim A et al (2023) Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 50:352–375. https://doi.org/10.1007/s00259-022-06001-6CrossRefPubMed

Pfaehler E, Zhovannik I, Wei L et al (2021) A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 20:69–75. https://doi.org/10.1016/j.phro.2021.10.007CrossRefPubMedPubMedCentral

Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029. https://doi.org/10.1148/ryai.2020200029

Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ 370:m3164. https://doi.org/10.1136/bmj.m3164

Rivera SC, Liu X, Chan A-W et al (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health 2:e549–e560. https://doi.org/10.1016/S2589-7500(20)30219-3CrossRefPubMedCentral

10.

Kocak B, Baessler B, Bakas S et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14(1):75. https://doi.org/10.1186/s13244-023-01415-8

11.

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141CrossRefPubMed

12.

Cerdá-Alberich L, Solana J, Mallol P et al (2023) MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging 14:11. https://doi.org/10.1186/s13244-022-01355-9CrossRefPubMedPubMedCentral

13.

Gidwani M, Chang K, Patel JB et al (2022) Inconsistent partitioning and unproductive feature associations yield idealized radiomic models. Radiology 220715. https://doi.org/10.1148/radiol.220715

14.

Wichtmann BD, Harder FN, Weiss K et al (2022) Influence of image processing on radiomic features from magnetic resonance imaging. Invest Radiol. https://doi.org/10.1097/RLI.0000000000000921CrossRefPubMed

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Duron L, Balvay D, Vande Perre S et al (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 14:e0213459. https://doi.org/10.1371/journal.pone.0213459

16.

Zwanenburg A, Vallières M, Abdalah MA et al (2020) The Image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338. https://doi.org/10.1148/radiol.2020191145CrossRefPubMed

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Reinke A, Tizabi MD, Sudre CH et al (2022) Common limitations of image processing metrics: a picture story. https://doi.org/10.48550/ARXIV.2104.05642

18.

van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 11:91. https://doi.org/10.1186/s13244-020-00887-2

19.

Demircioğlu A (2021) Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 12:172. https://doi.org/10.1186/s13244-021-01115-1CrossRefPubMedPubMedCentral

20.

Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49:1373–1379. https://doi.org/10.1016/s0895-4356(96)00236-3

21.

Riley RD, Snell KI, Ensor J et al (2019) Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med 38:1276–1296. https://doi.org/10.1002/sim.7992CrossRefPubMed

22.

Maier-Hein L, Reinke A, Godau P et al (2022) Metrics reloaded: pitfalls and recommendations for image analysis validation. https://doi.org/10.48550/arXiv.2206.01653

23.

Kocak B, Yardimci AH, Yuzkan S et al (2022) Transparency in artificial intelligence research: a systematic review of availability items related to open science in radiology and nuclear medicine. Acad Radiol S1076–6332(22):00635–00643. https://doi.org/10.1016/j.acra.2022.11.030CrossRef

Title: Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology
Authors: Burak Kocak
Leonid L. Chepelev
Linda C. Chu
Renato Cuocolo
Brendan S. Kelly
Philipp Seeböck
Yee Liang Thian
Robbert W. van Hamersvelt
Alan Wang
Stuart Williams
Jan Witowski
Zhongyi Zhang
Daniel Pinto dos Santos
Publication date: 26-06-2023
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 11/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-09768-w

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Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

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