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@ARTICLE{Kocak:1022151,
      author       = {Kocak, Burak and Akinci D’Antonoli, Tugba and Mercaldo,
                      Nathaniel and Alberich-Bayarri, Angel and Baessler, Bettina
                      and Ambrosini, Ilaria and Andreychenko, Anna E. and Bakas,
                      Spyridon and Beets-Tan, Regina G. H. and Bressem, Keno and
                      Buvat, Irene and Cannella, Roberto and Cappellini, Luca
                      Alessandro and Cavallo, Armando Ugo and Chepelev, Leonid L.
                      and Chu, Linda Chi Hang and Demircioglu, Aydin and deSouza,
                      Nandita M. and Dietzel, Matthias and Fanni, Salvatore
                      Claudio and Fedorov, Andrey and Fournier, Laure S. and
                      Giannini, Valentina and Girometti, Rossano and Groot Lipman,
                      Kevin B. W. and Kalarakis, Georgios and Kelly, Brendan S.
                      and Klontzas, Michail E. and Koh, Dow-Mu and Kotter, Elmar
                      and Lee, Ho Yun and Maas, Mario and Marti-Bonmati, Luis and
                      Müller, Henning and Obuchowski, Nancy and Orlhac, Fanny and
                      Papanikolaou, Nikolaos and Petrash, Ekaterina and Pfaehler,
                      Elisabeth and Pinto dos Santos, Daniel and Ponsiglione,
                      Andrea and Sabater, Sebastià and Sardanelli, Francesco and
                      Seeböck, Philipp and Sijtsema, Nanna M. and Stanzione,
                      Arnaldo and Traverso, Alberto and Ugga, Lorenzo and
                      Vallières, Martin and van Dijk, Lisanne V. and van
                      Griethuysen, Joost J. M. and van Hamersvelt, Robbert W. and
                      van Ooijen, Peter and Vernuccio, Federica and Wang, Alan and
                      Williams, Stuart and Witowski, Jan and Zhang, Zhongyi and
                      Zwanenburg, Alex and Cuocolo, Renato},
      title        = {{MET}hodological {R}adiom{IC}s {S}core ({METRICS}): a
                      quality scoring tool for radiomics research endorsed by
                      {E}u{S}o{MII}},
      journal      = {Insights into imaging},
      volume       = {15},
      number       = {1},
      issn         = {1869-4101},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2024-01272},
      pages        = {8},
      year         = {2024},
      abstract     = {Purpose: To propose a new quality scoring tool,
                      METhodological RadiomICs Score (METRICS), to assess and
                      improve research quality of radiomics studies.Methods: We
                      conducted an online modified Delphi study with a group of
                      international experts. It was performed in three consecutive
                      stages: Stage#1, item preparation; Stage#2, panel discussion
                      among EuSoMII Auditing Group members to identify the items
                      to be voted; and Stage#3, four rounds of the modified Delphi
                      exercise by panelists to determine the items eligible for
                      the METRICS and their weights. The consensus threshold was
                      $75\%.$ Based on the median ranks derived from expert panel
                      opinion and their rank-sum based conversion to importance
                      scores, the category and item weights were
                      calculated.Result: In total, 59 panelists from 19 countries
                      participated in selection and ranking of the items and
                      categories. Final METRICS tool included 30 items within 9
                      categories. According to their weights, the categories were
                      in descending order of importance: study design, imaging
                      data, image processing and feature extraction, metrics and
                      comparison, testing, feature processing, preparation for
                      modeling, segmentation, and open science. A web application
                      and a repository were developed to streamline the
                      calculation of the METRICS score and to collect feedback
                      from the radiomics community.Conclusion: In this work, we
                      developed a scoring tool for assessing the methodological
                      quality of the radiomics research, with a large
                      international panel and a modified Delphi protocol. With its
                      conditional format to cover methodological variations, it
                      provides a well-constructed framework for the key
                      methodological concepts to assess the quality of radiomic
                      research papers.Critical relevance statement: A quality
                      assessment tool, METhodological RadiomICs Score (METRICS),
                      is made available by a large group of international domain
                      experts, with transparent methodology, aiming at evaluating
                      and improving research quality in radiomics and machine
                      learning.Key points:• A methodological scoring tool,
                      METRICS, was developed for assessing the quality of
                      radiomics research, with a large international expert panel
                      and a modified Delphi protocol.• The proposed scoring tool
                      presents expert opinion-based importance weights of
                      categories and items with a transparent methodology for the
                      first time.• METRICS accounts for varying use cases, from
                      handcrafted radiomics to entirely deep learning-based
                      pipelines.• A web application has been developed to help
                      with the calculation of the METRICS score
                      (https://metricsscore.github.io/metrics/METRICS.html) and a
                      repository created to collect feedback from the radiomics
                      community (https://github.com/metricsscore/metrics).},
      cin          = {IAS-8},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 510 - Engineering Digital
                      Futures – Supercomputing, Data Management and Information
                      Security for Knowledge and Action (POF4-500)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-510},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38228979},
      UT           = {WOS:001143356000001},
      doi          = {10.1186/s13244-023-01572-w},
      url          = {https://juser.fz-juelich.de/record/1022151},
}