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@ARTICLE{Lococo:276861,
      author       = {F. Lococo and L. Boldrini and C.-D. Diepriye and J.
                      Evangelista and C. Nero and S. Flamini and A. Minucci and E.
                      De Paolis and E. Vita and A. Cesario and S. Annunziata and
                      M. L. Calcagni and M. Chiappetta and A. Cancellieri and A.
                      R. Larici and G. Cicchetti and E. Troost$^*$ and Á. Róza
                      and N. Farré and E. Öztürk and D. Van Doorne and F.
                      Leoncini and A. Urbani and R. Trisolini and E. Bria and A.
                      Giordano and G. Rindi and E. Sala and G. Tortora and V.
                      Valentini and S. Boccia and S. Margaritora and G. Scambia},
      title        = {{L}ung cancer multi-omics digital human avatars for
                      integrating precision medicine into clinical practice: the
                      {LANTERN} study.},
      journal      = {BMC cancer},
      volume       = {23},
      number       = {1},
      issn         = {1471-2407},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2023-01173},
      pages        = {540},
      year         = {2023},
      abstract     = {The current management of lung cancer patients has reached
                      a high level of complexity. Indeed, besides the traditional
                      clinical variables (e.g., age, sex, TNM stage), new omics
                      data have recently been introduced in clinical practice,
                      thereby making more complex the decision-making process.
                      With the advent of Artificial intelligence (AI) techniques,
                      various omics datasets may be used to create more accurate
                      predictive models paving the way for a better care in lung
                      cancer patients.The LANTERN study is a multi-center
                      observational clinical trial involving a multidisciplinary
                      consortium of five institutions from different European
                      countries. The aim of this trial is to develop accurate
                      several predictive models for lung cancer patients, through
                      the creation of Digital Human Avatars (DHA), defined as
                      digital representations of patients using various
                      omics-based variables and integrating well-established
                      clinical factors with genomic data, quantitative imaging
                      data etc. A total of 600 lung cancer patients will be
                      prospectively enrolled by the recruiting centers and
                      multi-omics data will be collected. Data will then be
                      modelled and parameterized in an experimental context of
                      cutting-edge big data analysis. All data variables will be
                      recorded according to a shared common ontology based on
                      variable-specific domains in order to enhance their direct
                      actionability. An exploratory analysis will then initiate
                      the biomarker identification process. The second phase of
                      the project will focus on creating multiple multivariate
                      models trained though advanced machine learning (ML) and AI
                      techniques for the specific areas of interest. Finally, the
                      developed models will be validated in order to test their
                      robustness, transferability and generalizability, leading to
                      the development of the DHA. All the potential clinical and
                      scientific stakeholders will be involved in the DHA
                      development process. The main goals aim of LANTERN project
                      are: i) To develop predictive models for lung cancer
                      diagnosis and histological characterization; (ii) to set up
                      personalized predictive models for individual-specific
                      treatments; iii) to enable feedback data loops for
                      preventive healthcare strategies and quality of life
                      management.The LANTERN project will develop a predictive
                      platform based on integration of multi-omics data. This will
                      enhance the generation of important and valuable information
                      assets, in order to identify new biomarkers that can be used
                      for early detection, improved tumor diagnosis and
                      personalization of treatment protocols.5420 - 0002485/23
                      from Fondazione Policlinico Universitario Agostino Gemelli
                      IRCCS - Università Cattolica del Sacro Cuore Ethics
                      Committee.clinicaltrial.gov - NCT05802771.},
      keywords     = {Artificial intelligence (AI) (Other) / Big data (Other) /
                      Digital human avatars (DHA) (Other) / Genomics (Other) /
                      Lung cancer (Other) / Machine learning (Other) / Personalize
                      medicine (Other) / Precision medicine (Other) / Radiomics
                      (Other) / System medicine (Other)},
      cin          = {DD01},
      ddc          = {610},
      cid          = {I:(DE-He78)DD01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37312079},
      doi          = {10.1186/s12885-023-10997-x},
      url          = {https://inrepo02.dkfz.de/record/276861},
}