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@ARTICLE{Herr:301997,
      author       = {F. L. Herr and C. Dascalescu and R. Ebner and M. L.
                      Schnitzer and M. P. Fabritius and C. Schmid-Tannwald and M.
                      J. Zacherl and V. Wenter and L. M. Unterrainer and M.
                      Brendel$^*$ and A. Holzgreve and R. A. Werner and C. J.
                      Auernhammer and C. Spitzweg and T. Knösel and T. Burkard
                      and J. Ricke and M. M. Heimer and G. T. Sheikh and C. C.
                      Cyran},
      title        = {{A}ssociation of integrated biomarkers and progression-free
                      survival prediction in patients with gastroenteropancreatic
                      neuroendocrine tumors undergoing [177{L}u]{L}u-{DOTA}-{TATE}
                      therapy},
      journal      = {Theranostics},
      volume       = {15},
      number       = {13},
      issn         = {1838-7640},
      address      = {Wyoming, NSW},
      publisher    = {Ivyspring},
      reportid     = {DKFZ-2025-01196},
      pages        = {6444 - 6453},
      year         = {2025},
      abstract     = {Integrated biomarkers that predict survival in patients
                      with gastroenteropancreatic neuroendocrine tumors (GEP-NET)
                      receiving peptidereceptor radionuclide therapy (PRRT) are
                      still limited. This study aims to identify predictors of
                      progression-free survival (PFS) in patientswith GEP-NET
                      undergoing two cycles of PRRT.Methods: This single-center
                      retrospective study included 178 patients with GEP-NET (G1
                      and G2) who received at least twoconsecutive cycles of PRRT
                      with [177Lu]Lu-DOTA-TATE and underwent somatostatin receptor
                      (SSTR)-PET/CT before and aftertherapy. At baseline, Krenning
                      score (KS) > 2, clinical, pathological and laboratory
                      parameters were collected and correlated to PFS.Survival
                      predictors were analyzed using univariate and multivariate
                      models. For goodness-of-fit analysis, the Akaike information
                      criterionand Harrell concordance index were determined. To
                      determine the impact on the regression model the Wald-Test
                      was performed.Results: In univariate analysis, KS 3 (vs. KS
                      4; HR, 2.02; $95\%$ CI, 1.27–3.22; p = 0.012), Ki-67 > 5
                      $\%$ (HR, 2.00; $95\%$ CI, 1.31–3.04; p =0.008), CgA > 200
                      ng/mL (HR, 1.77; $95\%$ CI, 1.14–2.76; p = 0.027) and NSE
                      > 35 ng/mL (HR, 2.37; $95\%$ CI, 1.44–3.89; p < 0.008)
                      weresignificantly associated with shorter PFS, with CgA
                      providing the highest C-index (0.6). In multivariate
                      analysis , KS 3 (vs. KS 4; HR, $1.94;95\%$ CI, 1.17–3.21;
                      p = 0.01), CgA > 200 ng/mL (HR, 1.76; CI, 1.08–2.87; p =
                      0.024), NSE > 35 ng/mL (HR, 1.98; $95\%$ CI, 1.17–3.36; p
                      =0.011), and Ki-67 > 5 $\%$ (HR, 1.89; $95\%$ CI,
                      1.18–3.02; p = 0.008) were significantly associated with
                      reduced PFS. Including KS intomultivariate analysis
                      significantly improved the Cox regression model performance,
                      as shown by a reduction in Akaike InformationCriterion
                      (592/596) and an increase in concordance index (0.66/0.65).
                      The Wald test for individual variables supported the
                      significance ofboth Ki-67 (7.1) and KS (6.7) as independent
                      predictors of PFS.Conclusions: NSE, CgA, KS and Ki-67
                      emerged as independent predictors of PFS in GEP-NET patients
                      scheduled for two cycles of PRRT,thereby emphasizing the
                      importance of integrated diagnostics including in- and
                      ex-vivo biomarkers to identify high-risk individuals proneto
                      disease progression.},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.7150/thno.112588},
      url          = {https://inrepo02.dkfz.de/record/301997},
}