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@ARTICLE{Lisson:305650,
      author       = {C. G. Lisson and M. Götz$^*$ and D. Wolf and S. Manoj and
                      L. Gallee and S. A. Schmidt and E. Tausch and C. Schneider
                      and S. Stilgenbauer and A. J. Beer and M. Beer and N.
                      Sollmann and C. S. Lisson},
      title        = {{N}on-{H}odgkin's lymphoma classification using 3{D}
                      radiomics machine learning models for precision imaging in
                      oncology.},
      journal      = {BMC medical imaging},
      volume       = {25},
      number       = {1},
      issn         = {1471-2342},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-02282},
      pages        = {435},
      year         = {2025},
      note         = {#EA:E230#},
      abstract     = {To apply quantitative imaging analysis for noninvasive
                      classification of the most frequent subtypes of Non-Hodgkin
                      Lymphoma (NHL) as a basis for a clinical imaging genomic
                      model to support therapeutic monitoring and clinical
                      decision making.In this single-center study, 201
                      treatment-naïve patients with biopsy-proven NHL (50 diffuse
                      large B-cell lymphoma [DLBCL], 51 mantle cell lymphoma
                      [MCL], 49 follicular lymphoma [FL], and 51 chronic
                      lymphocytic leukemia [CLL]) and 39 treatment-naïve
                      non-small cell lung cancer patients with positron emission
                      tomography (PET)/computed tomography (CT)-confirmed healthy
                      axillary lymph nodes (LNs) were retrospectively analyzed.
                      Three-dimensional (3D) segmentation and radiomic analysis of
                      pathologically enlarged nodes (n = 1,628) were performed on
                      contrast-enhanced CT scans, including healthy LNs as
                      references. Feature selection was performed using a random
                      forest (RF) classifier. Multiclass Classifier was performed
                      using a Light Gradient Boosting Machine (LGBM) classifier
                      for lymphoma subtype classification.Performance to classify
                      lymphoma from non-lymphoma and lymphoma subtypes was as
                      follows: lymphoma vs. non-lymphoma: area under the curve
                      (AUC) = 0.999; MCL vs. other NHL: AUC = 0.997; DLBCL vs.
                      other NHL: AUC = 0.971; CLL vs. other NHL: AUC = 0.956; FL
                      vs. other NHL: AUC = 0.892.Radiomics combined with
                      multiclass machine learning enables highly accurate,
                      non-invasive differentiation of the major NHL subtypes on
                      routine contrast-enhanced CT. By reliably separating
                      indolent from aggressive phenotypes, this approach lays the
                      groundwork for imaging-genomic models that could streamline
                      biopsy guidance, enhance therapeutic monitoring, and advance
                      precision oncology in lymphoma care.Conducted as a
                      single-centre, retrospective proof-of-concept with internal
                      patient-level cross-validation, these results are promising
                      and form the basis for a prospective multicentre study to
                      confirm generalisability and clinical utility.Accurate
                      lymphoma classification is essential for predicting clinical
                      behavior and guiding treatment. Imaging aids in disease
                      staging, with quantitative analysis showing promise in
                      predicting pathology and outcome. We explored machine
                      learning on imaging features for lymphoma classification,
                      thus enhancing clinical decisions.},
      keywords     = {Humans / Machine Learning / Lymphoma, Non-Hodgkin:
                      diagnostic imaging / Lymphoma, Non-Hodgkin: classification /
                      Lymphoma, Non-Hodgkin: pathology / Middle Aged / Female /
                      Male / Retrospective Studies / Aged / Positron Emission
                      Tomography Computed Tomography: methods / Imaging,
                      Three-Dimensional: methods / Adult / Aged, 80 and over /
                      Lymph Nodes: diagnostic imaging / Radiomics / Imaging
                      biomarkers. (Other) / Lymph nodes (Other) / Machine learning
                      (Other) / Non-Hodgkin lymphoma (Other) / Precision oncology
                      (Other) / Radiomics (Other)},
      cin          = {E230},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41168756},
      pmc          = {pmc:PMC12577418},
      doi          = {10.1186/s12880-025-02006-3},
      url          = {https://inrepo02.dkfz.de/record/305650},
}