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@ARTICLE{Vison:282721,
      author       = {G. Visonà and L. M. Spiller and S. Hahn and E.
                      Hattingen$^*$ and T. J. Vogl$^*$ and G. Schweikert and K.
                      Bankov and M. Demes$^*$ and H. Reis$^*$ and P. Wild$^*$ and
                      P. S. Zeiner$^*$ and F. Acker$^*$ and M. Sebastian$^*$ and
                      K. J. Wenger$^*$},
      title        = {{M}achine-{L}earning-{A}ided {P}rediction of {B}rain
                      {M}etastases {D}evelopment in {N}on-{S}mall-{C}ell {L}ung
                      {C}ancers.},
      journal      = {Clinical lung cancer},
      volume       = {24},
      number       = {8},
      issn         = {1525-7304},
      address      = {Dallas, Tex.},
      publisher    = {Cancer Information Group},
      reportid     = {DKFZ-2023-01847},
      pages        = {e311-e322},
      year         = {2023},
      note         = {2023 Dec;24(8):e311-e322},
      abstract     = {Non-small-cell lung cancer (NSCLC) shows a high incidence
                      of brain metastases (BM). Early detection is crucial to
                      improve clinical prospects. We trained and validated
                      classifier models to identify patients with a high risk of
                      developing BM, as they could potentially benefit from
                      surveillance brain MRI.Consecutive patients with an initial
                      diagnosis of NSCLC from January 2011 to April 2019 and an
                      in-house chest-CT scan (staging) were retrospectively
                      recruited at a German lung cancer center. Brain imaging was
                      performed at initial diagnosis and in case of neurological
                      symptoms (follow-up). Subjects lost to follow-up or still
                      alive without BM at the data cut-off point (12/2020) were
                      excluded. Covariates included clinical and/or
                      3D-radiomics-features of the primary tumor from staging
                      chest-CT. Four machine learning models for prediction (80/20
                      training) were compared. Gini Importance and SHAP were used
                      as measures of importance; sensitivity, specificity, area
                      under the precision-recall curve, and Matthew's Correlation
                      Coefficient as evaluation metrics.Three hundred and
                      ninety-five patients compromised the clinical cohort.
                      Predictive models based on clinical features offered the
                      best performance (tuned to maximize recall:
                      $sensitivity∼70\%,$ $specificity∼60\%).$ Radiomics
                      features failed to provide sufficient information, likely
                      due to the heterogeneity of imaging data. Adenocarcinoma
                      histology, lymph node invasion, and histological tumor grade
                      were positively correlated with the prediction of BM, age,
                      and squamous cell carcinoma histology were negatively
                      correlated. A subgroup discovery analysis identified 2
                      candidate patient subpopulations appearing to present a
                      higher risk of BM (female patients + adenocarcinoma
                      histology, adenocarcinoma patients + no other distant
                      metastases).Analysis of the importance of input features
                      suggests that the models are learning the relevant
                      relationships between clinical features/development of BM. A
                      higher number of samples is to be prioritized to improve
                      performance. Employed prospectively at initial diagnosis,
                      such models can help select high-risk subgroups for
                      surveillance brain MRI.},
      keywords     = {Interpretable machine learning (Other) / NSCLC (Other) /
                      Predictive models (Other) / Radiomics (Other) / Secondary
                      brain cancer (Other)},
      cin          = {FM01},
      ddc          = {610},
      cid          = {I:(DE-He78)FM01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37689579},
      doi          = {10.1016/j.cllc.2023.08.002},
      url          = {https://inrepo02.dkfz.de/record/282721},
}