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@ARTICLE{Sachpekidis:288854,
      author       = {C. Sachpekidis$^*$ and O. Enqvist and J. Ulén and A.
                      Kopp-Schneider$^*$ and L. Pan$^*$ and E. K. Mai and M.
                      Hajiyianni and M. Merz and M. S. Raab and A. Jauch and H.
                      Goldschmidt and L. Edenbrandt and A.
                      Dimitrakopoulou-Strauss$^*$},
      title        = {{A}rtificial intelligence-based, volumetric assessment of
                      the bone marrow metabolic activity in [18{F}]{FDG}
                      {PET}/{CT} predicts survival in multiple myeloma.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {51},
      number       = {8},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2024-00501},
      pages        = {2293-2307},
      year         = {2024},
      note         = {#EA:E060#LA:E060# / 2024 Jul;51(8):2293-2307},
      abstract     = {Multiple myeloma (MM) is a highly heterogeneous disease
                      with wide variations in patient outcome. [18F]FDG PET/CT can
                      provide prognostic information in MM, but it is hampered by
                      issues regarding standardization of scan interpretation. Our
                      group has recently demonstrated the feasibility of
                      automated, volumetric assessment of bone marrow (BM)
                      metabolic activity on PET/CT using a novel artificial
                      intelligence (AI)-based tool. Accordingly, the aim of the
                      current study is to investigate the prognostic role of
                      whole-body calculations of BM metabolism in patients with
                      newly diagnosed MM using this AI tool.Forty-four, previously
                      untreated MM patients underwent whole-body [18F]FDG PET/CT.
                      Automated PET/CT image segmentation and volumetric
                      quantification of BM metabolism were based on an initial
                      CT-based segmentation of the skeleton, its transfer to the
                      standardized uptake value (SUV) PET images, subsequent
                      application of different SUV thresholds, and refinement of
                      the resulting regions using postprocessing. In the present
                      analysis, ten different uptake thresholds (AI approaches),
                      based on reference organs or absolute SUV values, were
                      applied for definition of pathological tracer uptake and
                      subsequent calculation of the whole-body metabolic tumor
                      volume (MTV) and total lesion glycolysis (TLG). Correlation
                      analysis was performed between the automated PET values and
                      histopathological results of the BM as well as patients'
                      progression-free survival (PFS) and overall survival (OS).
                      Receiver operating characteristic (ROC) curve analysis was
                      used to investigate the discrimination performance of MTV
                      and TLG for prediction of 2-year PFS. The prognostic
                      performance of the new Italian Myeloma criteria for PET Use
                      (IMPeTUs) was also investigated.Median follow-up $[95\%$ CI]
                      of the patient cohort was 110 months [105-123 months].
                      AI-based BM segmentation and calculation of MTV and TLG were
                      feasible in all patients. A significant, positive, moderate
                      correlation was observed between the automated quantitative
                      whole-body PET/CT parameters, MTV and TLG, and BM plasma
                      cell infiltration for all ten [18F]FDG uptake thresholds.
                      With regard to PFS, univariable analysis for both MTV and
                      TLG predicted patient outcome reasonably well for all AI
                      approaches. Adjusting for cytogenetic abnormalities and BM
                      plasma cell infiltration rate, multivariable analysis also
                      showed prognostic significance for high MTV, which defined
                      pathological [18F]FDG uptake in the BM via the liver. In
                      terms of OS, univariable and multivariable analysis showed
                      that whole-body MTV, again mainly using liver uptake as
                      reference, was significantly associated with shorter
                      survival. In line with these findings, ROC curve analysis
                      showed that MTV and TLG, assessed using liver-based
                      cut-offs, could predict 2-year PFS rates. The application of
                      IMPeTUs showed that the number of focal hypermetabolic BM
                      lesions and extramedullary disease had an adverse effect on
                      PFS.The AI-based, whole-body calculations of BM metabolism
                      via the parameters MTV and TLG not only correlate with the
                      degree of BM plasma cell infiltration, but also predict
                      patient survival in MM. In particular, the parameter MTV,
                      using the liver uptake as reference for BM segmentation,
                      provides solid prognostic information for disease
                      progression. In addition to highlighting the prognostic
                      significance of automated, global volumetric estimation of
                      metabolic tumor burden, these data open up new perspectives
                      towards solving the complex problem of interpreting PET
                      scans in MM with a simple, fast, and robust method that is
                      not affected by operator-dependent interventions.},
      keywords     = {Artificial intelligence (Other) / Deep learning (Other) /
                      Metabolic tumor volume (MTV) (Other) / Multiple myeloma
                      (Other) / Patient survival (Other) / Prognosis (Other) /
                      Total lesion glycolysis (TLG) (Other) / [18F]FDG PET/CT
                      (Other)},
      cin          = {C060 / E060},
      ddc          = {610},
      cid          = {I:(DE-He78)C060-20160331 / I:(DE-He78)E060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38456971},
      doi          = {10.1007/s00259-024-06668-z},
      url          = {https://inrepo02.dkfz.de/record/288854},
}