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@ARTICLE{Boeke:275930,
      author       = {S. Boeke$^*$ and R. M. Winter and S. Leibfarth and M. A.
                      Krueger and G. Bowden and J. Cotton and B. J. Pichler and D.
                      Zips$^*$ and D. Thorwarth$^*$},
      title        = {{M}achine learning identifies multi-parametric functional
                      {PET}/{MR} imaging cluster to predict radiation resistance
                      in preclinical head and neck cancer models.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {50},
      number       = {10},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2023-00918},
      pages        = {3084-3096},
      year         = {2023},
      note         = {2023 Aug;50(10):3084-3096},
      abstract     = {Tumor hypoxia and other microenvironmental factors are key
                      determinants of treatment resistance. Hypoxia positron
                      emission tomography (PET) and functional magnetic resonance
                      imaging (MRI) are established prognostic imaging modalities
                      to identify radiation resistance in head-and-neck cancer
                      (HNC). The aim of this preclinical study was to develop a
                      multi-parametric imaging parameter specifically for focal
                      radiotherapy (RT) dose escalation using HNC xenografts of
                      different radiation sensitivities.A total of eight human HNC
                      xenograft models were implanted into 68 immunodeficient
                      mice. Combined PET/MRI using dynamic
                      [18F]-fluoromisonidazole (FMISO) hypoxia PET,
                      diffusion-weighted (DW), and dynamic contrast-enhanced MRI
                      was carried out before and after fractionated RT (10 ×
                      2 Gy). Imaging data were analyzed on voxel-basis using
                      principal component (PC) analysis for dynamic data and
                      apparent diffusion coefficients (ADCs) for DW-MRI. A data-
                      and hypothesis-driven machine learning model was trained to
                      identify clusters of high-risk subvolumes (HRSs) from
                      multi-dimensional (1-5D) pre-clinical imaging data before
                      and after RT. The stratification potential of each 1D to 5D
                      model with respect to radiation sensitivity was evaluated
                      using Cohen's d-score and compared to classical features
                      such as mean/peak/maximum standardized uptake values
                      (SUVmean/peak/max) and tumor-to-muscle-ratios (TMRpeak/max)
                      as well as minimum/valley/maximum/mean ADC.Complete 5D
                      imaging data were available for 42 animals. The final
                      preclinical model for HRS identification at baseline
                      yielding the highest stratification potential was defined in
                      3D imaging space based on ADC and two FMISO PCs ([Formula:
                      see text]). In 1D imaging space, only clusters of ADC
                      revealed significant stratification potential ([Formula: see
                      text]). Among all classical features, only ADCvalley showed
                      significant correlation to radiation resistance ([Formula:
                      see text]). After 2 weeks of RT, $FMISO_c1$ showed
                      significant correlation to radiation resistance ([Formula:
                      see text]).A quantitative imaging metric was described in a
                      preclinical study indicating that radiation-resistant
                      subvolumes in HNC may be detected by clusters of ADC and
                      FMISO using combined PET/MRI which are potential targets for
                      future functional image-guided RT dose-painting approaches
                      and require clinical validation.},
      keywords     = {Dose painting (Other) / Machine learning (Other) /
                      Multi-parametric functional imaging (Other) / PET/MRI
                      (Other) / Personalized radiation oncology (Other) /
                      Radiotherapy (Other)},
      cin          = {TU01},
      ddc          = {610},
      cid          = {I:(DE-He78)TU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37148296},
      doi          = {10.1007/s00259-023-06254-9},
      url          = {https://inrepo02.dkfz.de/record/275930},
}