Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.
Computer-assisted
Consensus development conference
Image processing
Terminology
Workflow
Journal
Insights into imaging
ISSN: 1869-4101
Titre abrégé: Insights Imaging
Pays: Germany
ID NLM: 101532453
Informations de publication
Date de publication:
03 Jun 2024
03 Jun 2024
Historique:
received:
22
12
2023
accepted:
20
04
2024
medline:
3
6
2024
pubmed:
3
6
2024
entrez:
2
6
2024
Statut:
epublish
Résumé
Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
Identifiants
pubmed: 38825600
doi: 10.1186/s13244-024-01704-w
pii: 10.1186/s13244-024-01704-w
doi:
Types de publication
Journal Article
Langues
eng
Pagination
124Informations de copyright
© 2024. The Author(s).
Références
Khoury M, Galea S (2016) Will precision medicine improve population health. JAMA 316:1357–1358. https://doi.org/10.1001/jama.2016.12260
doi: 10.1001/jama.2016.12260
pubmed: 27541310
pmcid: 6359904
Aerts H, Velazquez E, Leijenaar R et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006
doi: 10.1038/ncomms5006
pubmed: 24892406
Gutsche R, Lowis C, Ziemons K et al (2023) Automated brain tumor detection and segmentation for treatment response assessment using amino acid PET. J Nucl Med 64:1594–1602. https://doi.org/10.2967/jnumed.123.265725
doi: 10.2967/jnumed.123.265725
pubmed: 37562802
Meißner AK, Gutsche R, Galldiks N et al (2022) Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases. Neuro Oncol 24:1331–1340. https://doi.org/10.1093/neuonc/noab294
doi: 10.1093/neuonc/noab294
pubmed: 34935978
Meißner AK, Gutsche R, Galldiks N et al (2023) Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer. J Neurooncol 163:597–605. https://doi.org/10.1007/s11060-023-04367-7
doi: 10.1007/s11060-023-04367-7
pubmed: 37382806
pmcid: 10393847
Gillies R, Kinahan P, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169
doi: 10.1148/radiol.2015151169
pubmed: 26579733
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.2017.141
doi: 10.1038/nrclinonc.2017.141
pubmed: 28975929
Zwanenburg A, Vallières M, Abdalah M 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.2020191145
doi: 10.1148/radiol.2020191145
pubmed: 32154773
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 14:75. https://doi.org/10.1186/s13244-023-01415-8
doi: 10.1186/s13244-023-01415-8
pubmed: 37142815
pmcid: 10160267
Kocak B, Chepelev LL, Chu LC et al (2023) Assessment of radiomics research (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. Eur Radiol 33:7556–7560. https://doi.org/10.1007/s00330-023-09768-w
doi: 10.1007/s00330-023-09768-w
pubmed: 37358612
Dalkey N, Helmer O (1963) An experimental application of the DELPHI method to the use of experts. Manag Sci. https://doi.org/10.1287/mnsc.9.3.458
MAXQDA Software. VERBI Software (2020) Available via https://www.maxqda.com/ . Accessed 13 Nov 2022
Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Phys Med 38:122–139. https://doi.org/10.1016/j.ejmp.2017.05.071
doi: 10.1016/j.ejmp.2017.05.071
pubmed: 28595812
Chaddad A, Kucharczyk MJ, Daniel P et al (2019) Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol 9:374. https://doi.org/10.3389/fonc.2019.00374
doi: 10.3389/fonc.2019.00374
pubmed: 31165039
pmcid: 6536622
Fornacon-Wood I, Faivre-Finn C, O’Connor JPB, Price GJ (2020) Radiomics as a personalized medicine tool in lung cancer: separating the hope from the hype. Lung Cancer 146:197–208. https://doi.org/10.1016/j.lungcan.2020.05.028
doi: 10.1016/j.lungcan.2020.05.028
pubmed: 32563015
Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880–6890. https://doi.org/10.1007/s00330-019-06176-x
doi: 10.1007/s00330-019-06176-x
pubmed: 31227882
Hassani C, Varghese BA, Nieva J, Duddalwar V (2019) Radiomics in pulmonary lesion imaging. AJR Am J Roentgenol 212:497–504. https://doi.org/10.2214/AJR.18.20623
doi: 10.2214/AJR.18.20623
pubmed: 30620678
Horvat N, Bates DDB, Petkovska I (2019) Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY) 44:3764–3774. https://doi.org/10.1007/s00261-019-02042-y
doi: 10.1007/s00261-019-02042-y
pubmed: 31055615
Ibrahim A, Vallières M, Woodruff H et al (2019) Radiomics analysis for clinical decision support in nuclear medicine. Semin Nucl Med 49:438–449. https://doi.org/10.1053/j.semnuclmed.2019.06.005
doi: 10.1053/j.semnuclmed.2019.06.005
pubmed: 31470936
Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446. https://doi.org/10.1016/j.ejca.2011.11.036
doi: 10.1016/j.ejca.2011.11.036
pubmed: 22257792
pmcid: 4533986
Lee S-H, Park H, Ko ES (2020) Radiomics in breast imaging from techniques to clinical applications: a review. Korean J Radiol 21:779–792. https://doi.org/10.3348/kjr.2019.0855
doi: 10.3348/kjr.2019.0855
pubmed: 32524780
pmcid: 7289696
Machicado JD, Koay EJ, Krishna SG (2020) Radiomics for the diagnosis and differentiation of pancreatic cystic lesions. Diagnostics (Basel) 10:505. https://doi.org/10.3390/diagnostics10070505
doi: 10.3390/diagnostics10070505
pubmed: 32708348
Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61:488–495. https://doi.org/10.2967/jnumed.118.222893
doi: 10.2967/jnumed.118.222893
pubmed: 32060219
pmcid: 9374044
Moons KGM, Altman DG, Reitsma JB et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73. https://doi.org/10.7326/M14-0698
doi: 10.7326/M14-0698
pubmed: 25560730
Murray JM, Kaissis G, Braren R, Kleesiek J (2020) Wie funktioniert radiomics. Radiologe 60:32–41. https://doi.org/10.1007/s00117-019-00617-w
doi: 10.1007/s00117-019-00617-w
pubmed: 31820014
Scheckenbach K (2018) Radiomics: big data instead of biopsies in the future? Laryngorhinootologie 97:S114–S141. https://doi.org/10.1055/s-0043-121964
doi: 10.1055/s-0043-121964
pubmed: 29905355
pmcid: 6541032
Thawani R, McLane M, Beig N et al (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41. https://doi.org/10.1016/j.lungcan.2017.10.015
doi: 10.1016/j.lungcan.2017.10.015
pubmed: 29290259
Vallières M, Zwanenburg A, Badic B, Cheze Le Rest C, 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
doi: 10.2967/jnumed.117.200501
pubmed: 29175982
pmcid: 5807530
van Timmeren JES, 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
doi: 10.1186/s13244-020-00887-2
pubmed: 32785796
pmcid: 7423816
Wilson R, Devaraj A (2017) Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res 6:86–91. https://doi.org/10.21037/tlcr.2017.01.04
doi: 10.21037/tlcr.2017.01.04
pubmed: 28331828
pmcid: 5344835
Yang L, Gu D, Wei J et al (2019) A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 8:373–386. https://doi.org/10.1159/000494099
doi: 10.1159/000494099
pubmed: 31768346
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/s00259-019-04391-8
doi: 10.1007/s00259-019-04391-8
pubmed: 31240330
Bukowski M, Farkas R, Beyan O et al (2020) Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective. Eur Radiol 30:5510–5524. https://doi.org/10.1007/s00330-020-06874-x
doi: 10.1007/s00330-020-06874-x
pubmed: 32377810
pmcid: 7476980
Wichtmann BD, Albert S, Zhao W et al (2022) Are we there yet? The value of deep learning in a multicenter setting for response prediction of locally advanced rectal cancer to neoadjuvant chemoradiotherapy. Diagnostics (Basel) 12:1601. https://doi.org/10.3390/diagnostics12071601
doi: 10.3390/diagnostics12071601
pubmed: 35885506