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

• Published in: Insights into imaging 2022-10, Vol.13 (1), p.159-159, Article 159
• Main Authors: deSouza, Nandita M., van der Lugt, Aad, Deroose, Christophe M., Alberich-Bayarri, Angel, Bidaut, Luc, Fournier, Laure, Costaridou, Lena, Oprea-Lager, Daniela E., Kotter, Elmar, Smits, Marion, Mayerhoefer, Marius E., Boellaard, Ronald, Caroli, Anna, de Geus-Oei, Lioe-Fee, Kunz, Wolfgang G., Oei, Edwin H., Lecouvet, Frederic, Franca, Manuela, Loewe, Christian, Lopci, Egesta, Caramella, Caroline, Persson, Anders, Golay, Xavier, Dewey, Marc, O’Connor, James P. B., deGraaf, Pim, Gatidis, Sergios, Zahlmann, Gudrun
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Background noise; Biomarkers; Certification; Diagnostic Radiology; Digital imaging; Guideline; Health services; Image acquisition; Image contrast; Image reconstruction; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Logistics; Machine learning; mDelphi; Medical imaging; Medical research; Medicine; Medicine & Public Health; Modality-specific; Neuroradiology; Organ-specific; Performance tests; Radiology; Region of interest; Segmentation and standardisation; Spatial resolution; Training; Ultrasound
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-022-01287-4

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 ≤ 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.