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@ARTICLE{Almeida:289023,
      author       = {S. D. Almeida$^*$ and T. Norajitra$^*$ and C. T. Lüth$^*$
                      and T. Wald$^*$ and V. Weru$^*$ and M. Nolden$^*$ and P. F.
                      Jäger$^*$ and O. von Stackelberg and C. P. Heußel and O.
                      Weinheimer and J. Biederer and H.-U. Kauczor and K.
                      Maier-Hein$^*$},
      title        = {{C}apturing {COPD} heterogeneity: anomaly detection and
                      parametric response mapping comparison for phenotyping on
                      chest computed tomography.},
      journal      = {Frontiers in medicine},
      volume       = {11},
      issn         = {2296-858X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2024-00555},
      pages        = {1360706},
      year         = {2024},
      note         = {EA:E230#LA:E230#},
      abstract     = {Chronic obstructive pulmonary disease (COPD) poses a
                      substantial global health burden, demanding advanced
                      diagnostic tools for early detection and accurate
                      phenotyping. In this line, this study seeks to enhance COPD
                      characterization on chest computed tomography (CT) by
                      comparing the spatial and quantitative relationships between
                      traditional parametric response mapping (PRM) and a novel
                      self-supervised anomaly detection approach, and to unveil
                      potential additional insights into the dynamic transitional
                      stages of COPD.Non-contrast inspiratory and expiratory CT of
                      1,310 never-smoker and GOLD 0 individuals and COPD patients
                      (GOLD 1-4) from the COPDGene dataset were retrospectively
                      evaluated. A novel self-supervised anomaly detection
                      approach was applied to quantify lung abnormalities
                      associated with COPD, as regional deviations. These regional
                      anomaly scores were qualitatively and quantitatively
                      compared, per GOLD class, to PRM volumes (emphysema:
                      PRMEmph, functional small-airway disease: PRMfSAD) and to a
                      Principal Component Analysis (PCA) and Clustering, applied
                      on the self-supervised latent space. Its relationships to
                      pulmonary function tests (PFTs) were also evaluated.Initial
                      t-Distributed Stochastic Neighbor Embedding (t-SNE)
                      visualization of the self-supervised latent space
                      highlighted distinct spatial patterns, revealing clear
                      separations between regions with and without emphysema and
                      air trapping. Four stable clusters were identified among
                      this latent space by the PCA and Cluster Analysis. As the
                      GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and
                      Cluster 3 volumes exhibited escalating trends, contrasting
                      with a decline in Cluster 2. The patient-wise anomaly scores
                      significantly differed across GOLD stages (p < 0.01), except
                      for never-smokers and GOLD 0 patients. In contrast, PRMEmph,
                      PRMfSAD, and cluster classes showed fewer significant
                      differences. Pearson correlation coefficients revealed
                      moderate anomaly score correlations to PFTs (0.41-0.68),
                      except for the functional residual capacity and smoking
                      duration. The anomaly score was correlated with PRMEmph (r =
                      0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly
                      scores significantly improved fitting of PRM-adjusted
                      multivariate models for predicting clinical parameters (p <
                      0.001). Bland-Altman plots revealed that volume agreement
                      between PRM-derived volumes and clusters was not constant
                      across the range of measurements.Our study highlights the
                      synergistic utility of the anomaly detection approach and
                      traditional PRM in capturing the nuanced heterogeneity of
                      COPD. The observed disparities in spatial patterns, cluster
                      dynamics, and correlations with PFTs underscore the distinct
                      - yet complementary - strengths of these methods.
                      Integrating anomaly detection and PRM offers a promising
                      avenue for understanding of COPD pathophysiology,
                      potentially informing more tailored diagnostic and
                      intervention approaches to improve patient outcomes.},
      keywords     = {GOLD (Other) / airway disease (Other) / anomaly detection
                      (Other) / artificial intelligence (Other) / chronic
                      obstructive pulmonary disease (Other) / computed tomography
                      (Other) / emphysema (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:38495118},
      pmc          = {pmc:PMC10941845},
      doi          = {10.3389/fmed.2024.1360706},
      url          = {https://inrepo02.dkfz.de/record/289023},
}