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

Nandita M. deSouza*, Aad van der Lugt, European Organisation for Research and Treatment of Cancer, European Society of Radiology (ESR), Christophe M. Deroose, Angel Alberich-Bayarri, Luc Bidaut, Laure Fournier, Lena Costaridou, Daniela E. Oprea-Lager, Elmar Kotter, Marion Smits, Marius E. Mayerhoefer, Ronald Boellaard, Anna Caroli, Lioe Fee de Geus-Oei, Wolfgang G. Kunz, Edwin H. Oei, Frederic Lecouvet, Manuela FrancaChristian Loewe, Egesta Lopci, Caroline Caramella, Anders Persson, Xavier Golay, Marc Dewey, James P.B. O’Connor, Pim deGraaf, Sergios Gatidis, Gudrun Zahlmann

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number159
JournalInsights into Imaging
Issue number1
Publication statusPublished - 4 Oct 2022

Bibliographical note

Funding Information:
The following authors acknowledge funding that allowed their participation in this work: AA-B acknowledges financial support from the European Union’s Horizon 2020 research and innovation programme under grant agreement: PRIMAGE (nº 826494), ProCancer-I (nº 952159), ChAImeleon (nº 952172), and the Horizon Europe framework programme 2021 under grant agreement: RadioVal (nº 101057699). MEM is funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. XG is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. LF is funded in part by the French government under management of the Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute).

Publisher Copyright: © 2022, The Author(s).


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