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@ARTICLE{Hoberger:307579,
      author       = {M. Hoberger and R. L. Zuber and A. Burkhard-Meier and D. Di
                      Gioia and V. Jurinovic and M. Völkl and S. E. Güler and M.
                      Albertsmeier and A. Klein and H. R. Dürr and N.-S.
                      Schmidt-Hegemann and T. Knösel and W. G. Kunz and M. von
                      Bergwelt-Baildon$^*$ and L. H. Lindner and L. M.
                      Berclaz$^*$},
      title        = {{L}ong-term benefit from high-dose ifosfamide in sarcoma
                      depends on sustained prior control and timely intervention:
                      a machine learning analysis.},
      journal      = {Journal of cancer research and clinical oncology},
      volume       = {152},
      number       = {1},
      issn         = {0301-1585},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2026-00074},
      pages        = {34},
      year         = {2026},
      note         = {#DKTKZFB9#},
      abstract     = {High-dose ifosfamide (HD-IFO) remains an effective regimen
                      for advanced bone and soft tissue sarcomas, but predictors
                      of long-term benefit are poorly defined. This study
                      evaluated clinical outcomes and prognostic factors using
                      machine learning-assisted modeling in sarcoma patients
                      treated with HD-IFO at a high-volume academic center.We
                      retrospectively analyzed 26 patients with histologically
                      confirmed bone or soft tissue sarcoma who received HD-IFO
                      (≥ 12 g/m2 per cycle) between 2015 and 2025.
                      Progression-free survival (PFS) and overall survival (OS)
                      were estimated by the Kaplan-Meier method and compared
                      across RECIST response categories using log-rank testing.
                      Prognostic factors were identified using Least Absolute
                      Shrinkage and Selection Operator (LASSO) logistic regression
                      with leave-one-out cross-validation. The top three variables
                      were entered into multivariable logistic regression to
                      estimate odds ratios (ORs) for OS > 24 months.Median PFS and
                      OS from start of HD-IFO was 6.6 months $(95\%$ CI 4.4-9.8)
                      and 24.7 months $(95\%$ CI, 14.7-34.2), respectively.
                      Patients with progressive disease (PD) had significantly
                      shorter OS than those with partial response (PR; p = 0.0047)
                      or stable disease (SD; p = 0.0485). LASSO identified
                      intervention prior to progression, prior tumor control ≥
                      12 months, and absence of metastases as the strongest
                      predictors for OS > 24 months. In multivariable analysis,
                      intervention prior to progression (OR 24.18, $95\%$ CI
                      1.81-1001.27, p = 0.037) and prior tumor control ≥ 12
                      months (OR 25.39, $95\%$ CI 2.1-1008.9, p = 0.030)
                      independently predicted OS > 24 months.HD-IFO provides
                      durable disease control in selected sarcoma patients,
                      particularly those with sustained prior tumor control and
                      intervention prior to progression.},
      keywords     = {Humans / Ifosfamide: administration $\&$ dosage /
                      Ifosfamide: therapeutic use / Female / Male / Machine
                      Learning / Middle Aged / Retrospective Studies / Sarcoma:
                      drug therapy / Sarcoma: pathology / Sarcoma: mortality /
                      Adult / Antineoplastic Agents, Alkylating: administration
                      $\&$ dosage / Antineoplastic Agents, Alkylating: therapeutic
                      use / Aged / Young Adult / Prognosis / Bone Neoplasms: drug
                      therapy / Bone Neoplasms: pathology / Bone Neoplasms:
                      mortality / Adolescent / Bone sarcoma (Other) / High-dose
                      ifosfamide (Other) / Machine learning-assisted modeling
                      (Other) / Soft tissue sarcoma (Other) / Ifosfamide (NLM
                      Chemicals) / Antineoplastic Agents, Alkylating (NLM
                      Chemicals)},
      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},
      pubmed       = {pmid:41504936},
      doi          = {10.1007/s00432-025-06410-8},
      url          = {https://inrepo02.dkfz.de/record/307579},
}