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@ARTICLE{VanBergen:300233,
      author       = {T. D. Van Bergen and A. J. A. T. Braat and R. Hermsen and
                      J. G. Heetman and L. Wever and J. Lavalaye and M. Vinken and
                      C. D. Bahler and M. Tann and C. Kesch$^*$ and T. Telli$^*$
                      and P. K. Chiu and K. K. Wu and F. Zattoni and L.
                      Evangelista and F. Ceci and M. Miszczyk and P. Rajwa and F.
                      Barletta and G. Gandaglia and J. A. Van Basten and M. J.
                      Scheltema and H. H. E. Van Melick and R. C. N. Van den Bergh
                      and C. A. T. Van den Berg and G. Marra and T. F. W.
                      Soeterik},
      collaboration = {E. P. C. W. Party},
      title        = {{E}xternal validation of nomograms including {PSMA} {PET}
                      information for the prediction of lymph node involvement of
                      prostate cancer.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {52},
      number       = {10},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2025-00695},
      pages        = {3744-3756},
      year         = {2025},
      note         = {2025 Aug;52(10):3744-3756},
      abstract     = {Novel nomograms predicting lymph node involvement (LNI) of
                      prostate cancer (PCa) including PSMA PET information have
                      been developed. However, their predictive accuracy in
                      external populations is still unclear.To externally validate
                      four LNI nomograms including PSMA PET parameters (three
                      Muehlematter models and the Amsterdam-Brisbane-Sydney model)
                      as well as the Briganti 2012 and MSKCC nomograms.Patients
                      with histologically confirmed PCa undergoing preoperative
                      MRI and PSMA PET/CT before radical prostatectomy (RP) and
                      extended pelvic lymph node dissection (ePLND) were included.
                      Model discrimination (AUC), calibration and net benefit
                      using decision curve analysis were determined for each
                      nomogram.A total of 437 patients were included, comprising
                      $0.7\%$ with low-risk disease, $39.8\%$ with
                      intermediate-risk disease, and $59.5\%$ with high-risk
                      disease. Among them, 86 out of 437 $(19.7\%)$ had pN1
                      disease. The sensitivity and specificity of PSMA PET/CT for
                      the detection of LNI were $47.7\%$ $(95\%$ CI: 36.8-58.7)
                      and $95.4\%$ $(95\%$ CI: 92.7-97.4), respectively. Among
                      predictive models, the Amsterdam-Brisbane-Sydney model
                      achieved the highest discrimination (AUC: 0.81, $95\%$ CI:
                      0.76-0.86), followed by Muehlematter Model 1 (AUC: 0.79,
                      $95\%$ CI: 0.74-0.85), both with good calibration but slight
                      systematic overestimation of risks across all thresholds.
                      The MSKCC and Briganti 2012 models had AUCs of 0.68 $(95\%$
                      CI: 0.61-0.74) and 0.67 $(95\%$ CI: 0.61-0.73),
                      respectively, and both had moderate calibration. Decision
                      curve analysis indicated that the Amsterdam-Brisbane-Sydney
                      model provided superior net benefit across thresholds of
                      $5-20\%,$ followed by the Muehlematter Model 1 nomogram
                      showing benefit in the $14-20\%$ range. Using thresholds of
                      $8\%$ for the Amsterdam-Brisbane-Sydney nomogram and $15\%$
                      for Muehlematter Model 1, ePLND could be spared in $15\%$
                      and $16\%$ of patients, respectively, without missing any
                      LNI cases.External validation of the Muehlematter Model 1
                      and Amsterdam-Brisbane-Sydney nomograms for predicting LNI
                      confirmed their strong model discrimination, moderate
                      calibration, and good clinical utility, supporting their
                      reliability as tools to guide clinical decision-making.},
      keywords     = {External validation (Other) / Lymph node involvement
                      (Other) / Nomogram (Other) / PSMA PET/CT (Other) / Prostate
                      cancer (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:40172694},
      doi          = {10.1007/s00259-025-07241-y},
      url          = {https://inrepo02.dkfz.de/record/300233},
}