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@ARTICLE{Sachpekidis:277803,
      author       = {C. Sachpekidis$^*$ and O. Enqvist and J. Ulén and A.
                      Kopp-Schneider$^*$ and L. Pan$^*$ and A. Jauch and M.
                      Hajiyianni and L. John and N. Weinhold and S. Sauer and H.
                      Goldschmidt and L. Edenbrandt and A.
                      Dimitrakopoulou-Strauss$^*$},
      title        = {{A}pplication of an artificial intelligence-based tool in
                      [18{F}]{FDG} {PET}/{CT} for the assessment of bone marrow
                      involvement in multiple myeloma.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {50},
      number       = {12},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2023-01514},
      pages        = {3697-3708},
      year         = {2023},
      note         = {#EA:E060#LA:E060# / 2023 Oct;50(12):3697-3708},
      abstract     = {[18F]FDG PET/CT is an imaging modality of high performance
                      in multiple myeloma (MM). Nevertheless, the inter-observer
                      reproducibility in PET/CT scan interpretation may be
                      hampered by the different patterns of bone marrow (BM)
                      infiltration in the disease. Although many approaches have
                      been recently developed to address the issue of
                      standardization, none can yet be considered a standard
                      method in the interpretation of PET/CT. We herein aim to
                      validate a novel three-dimensional deep learning-based tool
                      on PET/CT images for automated assessment of the intensity
                      of BM metabolism in MM patients.Whole-body [18F]FDG PET/CT
                      scans of 35 consecutive, previously untreated MM patients
                      were studied. All patients were investigated in the context
                      of an open-label, multicenter, randomized,
                      active-controlled, phase 3 trial (GMMG-HD7). Qualitative
                      (visual) analysis classified the PET/CT scans into three
                      groups based on the presence and number of focal
                      [18F]FDG-avid lesions as well as the degree of diffuse
                      [18F]FDG uptake in the BM. The proposed automated method for
                      BM metabolism assessment is based on an initial CT-based
                      segmentation of the skeleton, its transfer to the SUV PET
                      images, the subsequent application of different SUV
                      thresholds, and refinement of the resulting regions using
                      postprocessing. In the present analysis, six different SUV
                      thresholds (Approaches 1-6) were applied for the definition
                      of pathological tracer uptake in the skeleton [Approach 1:
                      liver SUVmedian × 1.1 (axial skeleton), gluteal muscles
                      SUVmedian × 4 (extremities). Approach 2: liver SUVmedian ×
                      1.5 (axial skeleton), gluteal muscles SUVmedian × 4
                      (extremities). Approach 3: liver SUVmedian × 2 (axial
                      skeleton), gluteal muscles SUVmedian × 4 (extremities).
                      Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton),
                      ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the
                      resulting masks, subsequent calculations of the whole-body
                      metabolic tumor volume (MTV) and total lesion glycolysis
                      (TLG) in each patient were performed. A correlation analysis
                      was performed between the automated PET values and the
                      results of the visual PET/CT analysis as well as the
                      histopathological, cytogenetical, and clinical data of the
                      patients.BM segmentation and calculation of MTV and TLG
                      after the application of the deep learning tool were
                      feasible in all patients. A significant positive correlation
                      (p < 0.05) was observed between the results of the visual
                      analysis of the PET/CT scans for the three patient groups
                      and the MTV and TLG values after the employment of all six
                      [18F]FDG uptake thresholds. In addition, there were
                      significant differences between the three patient groups
                      with regard to their MTV and TLG values for all applied
                      thresholds of pathological tracer uptake. Furthermore, we
                      could demonstrate a significant, moderate, positive
                      correlation of BM plasma cell infiltration and plasma levels
                      of β2-microglobulin with the automated quantitative PET/CT
                      parameters MTV and TLG after utilization of Approaches 1, 2,
                      4, and 5.The automated, volumetric, whole-body PET/CT
                      assessment of the BM metabolic activity in MM is feasible
                      with the herein applied method and correlates with
                      clinically relevant parameters in the disease. This
                      methodology offers a potentially reliable tool in the
                      direction of optimization and standardization of PET/CT
                      interpretation in MM. Based on the present promising
                      findings, the deep learning-based approach will be further
                      evaluated in future prospective studies with larger patient
                      cohorts.},
      keywords     = {Artificial intelligence (Other) / Deep learning (Other) /
                      Metabolic tumor volume (MTV) (Other) / Multiple myeloma
                      (Other) / Objective quantification (Other) / Total lesion
                      glycolysis (TLG) (Other) / [18F]FDG PET/CT (Other)},
      cin          = {E060 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)E060-20160331 / I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37493665},
      doi          = {10.1007/s00259-023-06339-5},
      url          = {https://inrepo02.dkfz.de/record/277803},
}