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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006221naa a2200649 4500
001oai:DiVA.org:liu-189471
003SwePub
008221025s2022 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1894712 URI
0247 a https://doi.org/10.1186/s13244-022-01287-42 DOI
040 a (SwePub)liu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a DeSouza, Nandita M.u Inst Canc Res, England; Royal Marsden NHS Fdn Trust, England4 aut
2451 0a Standardised lesion segmentation for imaging biomarker quantitation :b a consensus recommendation from ESR and EORTC
264 c 2022-10-04
264 1b Springer,c 2022
338 a electronic2 rdacarrier
500 a 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]
520 a 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.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
653 a Segmentation and standardisation; mDelphi; Region of interest; Organ-specific; Modality-specific
7001 a van Der Lugt, Aadu Univ Med Ctr, Netherlands4 aut
7001 a Deroose, Christophe M.u Univ Hosp Leuven, Belgium; Katholieke Univ Leuven, Belgium4 aut
7001 a Alberich-Bayarri, Angelu Quantitat Imaging Biomarkers Med QUIBIM, Spain4 aut
7001 a Bidaut, Lucu Univ Lincoln, England4 aut
7001 a Fournier, Laureu Univ Paris, France4 aut
7001 a Costaridou, Lenau Univ Patras, Greece4 aut
7001 a Oprea-Lager, Daniela E.u Vrije Univ Amsterdam, Netherlands4 aut
7001 a Kotter, Elmaru Univ Med Ctr Freiburg, Germany4 aut
7001 a Smits, Marionu Univ Med Ctr, Netherlands4 aut
7001 a Mayerhoefer, Marius E.u Med Univ Vienna, Austria; Mem Sloan Kettering Canc Ctr, NY 10021 USA4 aut
7001 a Boellaard, Ronaldu Vrije Univ Amsterdam, Netherlands4 aut
7001 a Caroli, Annau Ist Ric Farmacol Mario Negri IRCCS, Italy4 aut
7001 a De Geus-Oei, Lioe-Feeu Leiden Univ, Netherlands; Univ Twente, Netherlands4 aut
7001 a Kunz, Wolfgang G.u Ludwig Maximilians Univ Munchen, Germany4 aut
7001 a Oei, Edwin H.u Univ Med Ctr, Netherlands4 aut
7001 a Lecouvet, Fredericu Univ Catholique Louvain UCLouvain, Belgium4 aut
7001 a Franca, Manuelau Univ Porto, Portugal4 aut
7001 a Loewe, Christianu Med Univ Vienna, Austria4 aut
7001 a Lopci, Egestau IRCCS Humanitas Res Hosp, Italy4 aut
7001 a Caramella, Carolineu Univ Paris Saclay, France4 aut
7001 a Persson, Andersu Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping4 aut0 (Swepub:liu)andpe75
7001 a Golay, Xavieru UCL, England4 aut
7001 a Dewey, Marcu Charite Univ Med Berlin, Germany4 aut
7001 a OConnor, James P. B.u Inst Canc Res, England; Royal Marsden NHS Fdn Trust, England4 aut
7001 a DeGraaf, Pimu Vrije Univ Amsterdam, Netherlands4 aut
7001 a Gatidis, Sergiosu Univ Tubingen, Germany4 aut
7001 a Zahlmann, Gudrunu Radiol Soc North Amer RSNA, IL USA4 aut
7102 a Inst Canc Res, England; Royal Marsden NHS Fdn Trust, Englandb Univ Med Ctr, Netherlands4 org
7730 t Insights into Imagingd : Springerg 13:1q 13:1x 1869-4101
8564 u https://liu.diva-portal.org/smash/get/diva2:1706097/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-189471
8564 8u https://doi.org/10.1186/s13244-022-01287-4

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