Standardised lesion segmentation for imaging biomarker quantitation:a consensus recommendation from ESR and EORTC

Autor: DeSouza, N.M., Lugt, A. van der, Deroose, C.M., Alberich-Bayarri, A., Bidaut, L., Fournier, L., Costaridou, L., Oprea-Lager, D.E., Kotter, E., Smits, M., Mayerhoefer, M.E., Boellaard, R., Caroli, A., Geus-Oei, L.F. de, Kunz, W.G., Oei, E.H., Lecouvet, F., Franca, M., Loewe, C., Lopci, E., Caramella, C., Persson, A., Golay, X., Dewey, M., O'Connor, J.P.B., DeGraaf, P., Gatidis, S., Zahlmann, G., European Soc Radiology, European Org Res Treatment Canc
Přispěvatelé: Radiology and nuclear medicine, AII - Cancer immunology, AII - Inflammatory diseases, CCA - Cancer biology and immunology, CCA - Imaging and biomarkers, Amsterdam Neuroscience - Brain Imaging, Radiology & Nuclear Medicine
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: European Society of Radiology & European Organisation for Research and Treatment of Cancer 2022, ' Standardised lesion segmentation for imaging biomarker quantitation : a consensus recommendation from ESR and EORTC ', Insights into Imaging, vol. 13, no. 1, 159, pp. 159 . https://doi.org/10.1186/s13244-022-01287-4, https://doi.org/10.1186/s13244-022-01287-4
Insights into Imaging, 13(1):159. Springer Science and Business Media Deutschland GmbH
Insights into Imaging, 13(1):159. Springer Science+Business Media
Insights into Imaging, 13(1). SPRINGER
ISSN: 1869-4101
Popis: Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]
Databáze: OpenAIRE
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