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@ARTICLE{Almeida:286588,
      author       = {S. D. Almeida$^*$ and T. Norajitra$^*$ and C. Lüth$^*$ and
                      T. Wald$^*$ and V. Weru$^*$ and M. Nolden$^*$ and P.
                      Jäger$^*$ and O. von Stackelberg and C. P. Heußel and O.
                      Weinheimer and J. Biederer and H.-U. Kauczor and K.
                      Maier-Hein$^*$},
      title        = {{P}rediction of disease severity in {COPD}: a deep learning
                      approach for anomaly-based quantitative assessment of chest
                      {CT}.},
      journal      = {European radiology},
      volume       = {34},
      number       = {7},
      issn         = {0938-7994},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2023-02805},
      pages        = {4379-4392},
      year         = {2024},
      note         = {#EA:E230#LA:E230# / 2024 Jul;34(7):4379-4392},
      abstract     = {To quantify regional manifestations related to COPD as
                      anomalies from a modeled distribution of normal-appearing
                      lung on chest CT using a deep learning (DL) approach, and to
                      assess its potential to predict disease severity.Paired
                      inspiratory/expiratory CT and clinical data from COPDGene
                      and COSYCONET cohort studies were included. COPDGene data
                      served as training/validation/test data sets (N =
                      3144/786/1310) and COSYCONET as external test set (N = 446).
                      To differentiate low-risk (healthy/minimal disease, [GOLD
                      0]) from COPD patients (GOLD 1-4), the self-supervised DL
                      model learned semantic information from 50 × 50 × 50 voxel
                      samples from segmented intact lungs. An anomaly detection
                      approach was trained to quantify lung abnormalities related
                      to COPD, as regional deviations. Four supervised DL models
                      were run for comparison. The clinical and radiological
                      predictive power of the proposed anomaly score was assessed
                      using linear mixed effects models (LMM).The proposed
                      approach achieved an area under the curve of 84.3 ± 0.3 (p
                      < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for
                      COSYCONET, outperforming supervised models even when
                      including only inspiratory CT. Anomaly scores significantly
                      improved fitting of LMM for predicting lung function, health
                      status, and quantitative CT features (emphysema/air
                      trapping; p < 0.001). Higher anomaly scores were
                      significantly associated with exacerbations for both cohorts
                      (p < 0.001) and greater dyspnea scores for COPDGene (p <
                      0.001).Quantifying heterogeneous COPD manifestations as
                      anomaly offers advantages over supervised methods and was
                      found to be predictive for lung function impairment and
                      morphology deterioration.Using deep learning, lung
                      manifestations of COPD can be identified as deviations from
                      normal-appearing chest CT and attributed an anomaly score
                      which is consistent with decreased pulmonary function,
                      emphysema, and air trapping.• A self-supervised DL anomaly
                      detection method discriminated low-risk individuals and COPD
                      subjects, outperforming classic DL methods on two datasets
                      (COPDGene AUC = $84.3\%,$ COSYCONET AUC = $76.3\%).$ • Our
                      contrastive task exhibits robust performance even without
                      the inclusion of expiratory images, while voxel-based
                      methods demonstrate significant performance enhancement when
                      incorporating expiratory images, in the COPDGene dataset.
                      • Anomaly scores improved the fitting of linear mixed
                      effects models in predicting clinical parameters and imaging
                      alterations (p < 0.001) and were directly associated with
                      clinical outcomes (p < 0.001).},
      keywords     = {Artificial intelligence (Other) / Chronic obstructive
                      pulmonary disease (Other) / Computed tomography (Other) /
                      Deep learning (Other)},
      cin          = {E230 / E290 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)E290-20160331 /
                      I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38150075},
      doi          = {10.1007/s00330-023-10540-3},
      url          = {https://inrepo02.dkfz.de/record/286588},
}