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@ARTICLE{Gromke:307433,
      author       = {T. Gromke and J. Durand$^*$ and T. T. Mueller and F.
                      Neumaier and S. T. Liffers$^*$ and H. Richly and M. Grubert
                      and J. Haubold and J. Theysohn and H. Kalkavan and N. E.
                      Bechrakis and M. Schuler$^*$ and R. Braren$^*$ and B. M.
                      Schaarschmidt$^*$ and J. T. Siveke$^*$},
      title        = {{C}linical and radiomics parameter prognostication in
                      metastatic uveal melanoma patients treated with hepatic
                      arterial infusion chemotherapy.},
      journal      = {The oncologist},
      volume       = {31},
      number       = {1},
      issn         = {1083-7159},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-03032},
      pages        = {oyaf385},
      year         = {2026},
      note         = {Volume 31, Issue 1, January 2026, oyaf385, Published:22
                      December 2025},
      abstract     = {Metastatic uveal melanoma (MUM) has a poor prognosis, but
                      hepatic arterial infusion chemotherapy (HAIC) may improve
                      outcomes in patients with hepatic metastases. To identify
                      reliable prognostic factors for patient stratification and
                      treatment allocation, we analyzed the clinical and imaging
                      data from a large single-center cohort using machine
                      learning (ML) models.Pre- and post first treatment clinical
                      data of 235 patients with MUM treated with HAIC between 2009
                      and 2019 were retrospectively analyzed using Cox regression
                      to identify prognostic factors for overall survival (OS) and
                      time to change treatment strategy (TTCS). Furthermore, ML
                      models were trained on clinical and computed tomography (CT)
                      data for endpoint prediction.Pre-treatment multivariate
                      analysis identified elevated lactate dehydrogenase (LDH)
                      (OS: 6.5 vs. 16.4 months, hazard ratio (HR)=1.87, p = 0.006)
                      and gamma-glutamyl transpeptidase (GGT) (OS: 7.6 vs. 16.4
                      months, HR = 1.67, p = 0.012) as prognostic factors for
                      inferior OS. Decreased albumin (TTCS: 1.3 vs. 6.1 months, HR
                      = 6.26, p < 0.001) and elevated LDH (TTCS: 2.9 vs. 7.6
                      months, HR = 1.72, p = 0.011) and alanine aminotransferase
                      (ALT) (TTCS: 3.7 vs. 6.4 months, HR = 1.65, p = 0.004)
                      predicted shorter TTCS. Scoring enhanced the power of the
                      prognosticators for OS and TTCS. Post first treatment
                      multivariate analysis emphasized the importance of
                      inflammation management and liver protection. ML models
                      incorporating radiomics features from base line CT imaging
                      were not superior to models based on pre-treatment clinical
                      data alone.We identified independent but synergistic
                      prognostic factors for outcome stratification to guide
                      treatment decisions and optimize patient management.
                      ML-based radiomics features did not significantly enhance
                      prognostic performance.},
      keywords     = {hepatic arterial infusion chemotherapy (Other) /
                      independent prognostic factors (Other) / machine learning
                      (Other) / metastatic uveal melanoma (Other) / multivariate
                      analysis (Other) / radiomics (Other)},
      cin          = {ED01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41429572},
      doi          = {10.1093/oncolo/oyaf385},
      url          = {https://inrepo02.dkfz.de/record/307433},
}