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@ARTICLE{Kim:274080,
      author       = {M. Kim and R. Seifert$^*$ and J. Fragemann and D. Kersting
                      and J. Murray and F. Jonske and K. L. Pomykala and J. Egger
                      and W. P. Fendler and K. Herrmann$^*$ and J. Kleesiek$^*$},
      title        = {{E}valuation of thresholding methods for the quantification
                      of [68{G}a]{G}a-{PSMA}-11 {PET} molecular tumor volume and
                      their effect on survival prediction in patients with
                      advanced prostate cancer undergoing [177{L}u]{L}u-{PSMA}-617
                      radioligand therapy.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {50},
      number       = {7},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2023-00426},
      pages        = {2196-2209},
      year         = {2023},
      note         = {2023 Jun;50(7):2196-2209},
      abstract     = {The aim of this study was to systematically evaluate the
                      effect of thresholding algorithms used in computer vision
                      for the quantification of prostate-specific membrane antigen
                      positron emission tomography (PET) derived tumor volume
                      (PSMA-TV) in patients with advanced prostate cancer. The
                      results were validated with respect to the prognostication
                      of overall survival in patients with advanced-stage prostate
                      cancer.A total of 78 patients who underwent
                      [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to
                      December 2020 were retrospectively included in this study.
                      [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide
                      therapy, were used for the analysis of thresholding
                      algorithms. All PET images were first analyzed
                      semi-automatically using a pre-evaluated, proprietary
                      software solution as the baseline method. Subsequently, five
                      histogram-based thresholding methods and two local adaptive
                      thresholding methods that are well established in computer
                      vision were applied to quantify molecular tumor volume. The
                      resulting whole-body molecular tumor volumes were validated
                      with respect to the prognostication of overall patient
                      survival as well as their statistical correlation to the
                      baseline methods and their performance on standardized
                      phantom scans.The whole-body PSMA-TVs, quantified using
                      different thresholding methods, demonstrate a high positive
                      correlation with the baseline methods. We observed the
                      highest correlation with generalized histogram thresholding
                      (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and
                      Sauvola thresholding (r = 0.974, p < 0.001) and the lowest
                      correlation with Multiotsu (r = 0.877, p < 0.001) and Yen
                      thresholding methods (r = 0.878, p < 0.001). The median
                      survival time of all patients was 9.87 months $(95\%$ CI
                      [9.3 to 10.13]). Stratification by median whole-body PSMA-TV
                      resulted in a median survival time from 11.8 to 13.5 months
                      for the patient group with lower tumor burden and 6.5 to 6.6
                      months for the patient group with higher tumor burden. The
                      patient group with lower tumor burden had significantly
                      higher probability of survival (p < 0.00625) in eight out of
                      nine thresholding methods (Fig. 2); those methods were
                      SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p =
                      0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p =
                      0.0001), Otsu (p = 0.0053), and Li thresholding (p =
                      0.0053).Thresholding methods commonly used in computer
                      vision are promising tools for the semiautomatic
                      quantification of whole-body PSMA-TV in
                      [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven
                      thresholding strategy is less arbitrary and less prone to
                      biases than thresholding with predefined values, potentially
                      improving the application of whole-body PSMA-TV as an
                      imaging biomarker.},
      keywords     = {Image biomarker (Other) / PSMA PET/CT (Other) / Prostate
                      cancer (Other) / Thresholding (Other) / Tumor volume
                      (Other)},
      cin          = {ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36859618},
      doi          = {10.1007/s00259-023-06163-x},
      url          = {https://inrepo02.dkfz.de/record/274080},
}