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@ARTICLE{Starke:267564,
      author       = {S. Starke$^*$ and A. A. Zwanenburg-Bezemer$^*$ and K.
                      Leger$^*$ and K. Zöphel and J. Kotzerke and M. Krause$^*$
                      and M. Baumann$^*$ and E. G. C. Troost$^*$ and S. Löck$^*$},
      title        = {{L}ongitudinal and {M}ultimodal {R}adiomics {M}odels for
                      {H}ead and {N}eck {C}ancer {O}utcome {P}rediction.},
      journal      = {Cancers},
      volume       = {15},
      number       = {3},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2023-00342},
      pages        = {673},
      year         = {2023},
      abstract     = {Radiomics analysis provides a promising avenue towards the
                      enabling of personalized radiotherapy. Most frequently,
                      prognostic radiomics models are based on features extracted
                      from medical images that are acquired before treatment.
                      Here, we investigate whether combining data from multiple
                      timepoints during treatment and from multiple imaging
                      modalities can improve the predictive ability of radiomics
                      models. We extracted radiomics features from computed
                      tomography (CT) images acquired before treatment as well as
                      two and three weeks after the start of radiochemotherapy for
                      55 patients with locally advanced head and neck squamous
                      cell carcinoma (HNSCC). Additionally, we obtained features
                      from FDG-PET images taken before treatment and three weeks
                      after the start of therapy. Cox proportional hazards models
                      were then built based on features of the different image
                      modalities, treatment timepoints, and combinations thereof
                      using two different feature selection methods in a five-fold
                      cross-validation approach. Based on the cross-validation
                      results, feature signatures were derived and their
                      performance was independently validated. Discrimination
                      regarding loco-regional control was assessed by the
                      concordance index (C-index) and log-rank tests were
                      performed to assess risk stratification. The best prognostic
                      performance was obtained for timepoints during treatment for
                      all modalities. Overall, CT was the best discriminating
                      modality with an independent validation C-index of 0.78 for
                      week two and weeks two and three combined. However, none of
                      these models achieved statistically significant patient
                      stratification. Models based on FDG-PET features from week
                      three provided both satisfactory discrimination (C-index =
                      0.61 and 0.64) and statistically significant stratification
                      (p=0.044 and p<0.001), but produced highly imbalanced risk
                      groups. After independent validation on larger datasets, the
                      value of (multimodal) radiomics models combining several
                      imaging timepoints should be prospectively assessed for
                      personalized treatment strategies.},
      keywords     = {Cox proportional hazards (Other) / computed tomography
                      (Other) / head and neck cancer (Other) / loco-regional
                      control (Other) / longitudinal imaging (Other) / positron
                      emission tomography (Other) / radiomics (Other) / survival
                      analysis (Other)},
      cin          = {DD01 / E220 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)DD01-20160331 / I:(DE-He78)E220-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36765628},
      doi          = {10.3390/cancers15030673},
      url          = {https://inrepo02.dkfz.de/record/267564},
}