Objective. To determine consensus among an international, multidisciplinary group of experts regarding defini-tions of spinal osteoarthritis for research and for clinical practice.Methods. A 15... Show moreObjective. To determine consensus among an international, multidisciplinary group of experts regarding defini-tions of spinal osteoarthritis for research and for clinical practice.Methods. A 15-member, multidisciplinary steering committee generated 117 statements for a 3-round Delphi study. Experts in back pain and/or osteoarthritis were identified and invited to participate. In round 1, participants could propose additional statements for voting. All statements were rated on a 1-9 Likert scale, and consensus was set at >= 70% of respondents agreeing or disagreeing with the statement and < 15% of respondents providing the opposite response.Results. In total, 255 experts from 11 different professional backgrounds were invited. From 173 available experts, 116 consented to participate. In round 1, 103 participants completed the survey, followed by 85 of 111 participants in round 2 (77%) and 87 of 101 participants in round 3 (86%). One-third of participants were from Europe (30%), most were male (58%), one-fifth were physical therapists (21%), and over one-third had been in their profession for 11-20 years (35%). Of 131 statements, consensus was achieved for 71 statements (54%): 53 in agreement (75%) and 18 in disagreement (25%).Conclusion. Although there was consensus for statements for definitions of spinal osteoarthritis that were analogous to definitions of osteoarthritis in appendicular joints, a future definition still needs refinement. Importantly, this Delphi highlighted that a future definition should be considered across a spectrum of structural changes and patient symptoms and expressed on a progressive scale. Show less
DeSouza, N.M.; Lugt, A. van der; Deroose, C.M.; Alberich-Bayarri, A.; Bidaut, L.; Fournier, L.; ... ; European Org Res Treatment Canc 2022
Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing... Show moreBackground 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 <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified. Show less
Fournier, L.; Costaridou, L.; Bidaut, L.; Michoux, N.; Lecouvet, F.E.; Geus-Oei, L.F. de; ... ; European Soc Radiology 2021
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before... Show moreExisting quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Show less
Lankhorst, N.E.; Damen, J.; Oei, E.H.; Verhaar, J.A.N.; Kloppenburg, M.; Bierma-Zeinstra, S.M.A.; Middelkoop, M. van 2017
