@article{LaquaWoznickiBleyetal.2023,
  author    = {Laqua, Fabian Christopher and Woznicki, Piotr and Bley, Thorsten A. and Sch{\"o}neck, Mirjam and Rinneburger, Miriam and Weisthoff, Mathilda and Schmidt, Matthias and Persigehl, Thorsten and Iuga, Andra-Iza and Baeßler, Bettina},
  title     = {Transfer-learning deep radiomics and hand-crafted radiomics for classifying lymph nodes from contrast-enhanced computed tomography in lung cancer},
  series = {Cancers},
  volume    = {15},
  journal   = {Cancers},
  number    = {10},
  issn      = {2072-6694},
  doi       = {10.3390/cancers15102850},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319231},
  year      = {2023},
  abstract  = {Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced \(^{18}\)F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional "hand-crafted" radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3\%) PET-positive] from 100 patients [49\% female; mean age 65, SD: 14] with lung cancer (60\% adenocarcinoma, 21\% plate epithelial carcinoma, 8\% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36\%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865-0.878), SBS 35.8 (34.2-37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.},
  language  = {en}
}
@article{GrunzKunzBaumannetal.2023,
  author    = {Grunz, Jan-Peter and Kunz, Andreas Steven and Baumann, Freerk T. and Hasenclever, Dirk and Sieren, Malte Maria and Heldmann, Stefan and Bley, Thorsten Alexander and Einsele, Hermann and Knop, Stefan and Jundt, Franziska},
  title     = {Assessing osteolytic lesion size on sequential CT scans is a reliable study endpoint for bone remineralization in newly diagnosed multiple myeloma},
  series = {Cancers},
  volume    = {15},
  journal   = {Cancers},
  number    = {15},
  issn      = {2072-6694},
  doi       = {10.3390/cancers15154008},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-362526},
  year      = {2023},
  abstract  = {Multiple myeloma (MM) frequently induces persisting osteolytic manifestations despite hematologic treatment response. This study aimed to establish a biometrically valid study endpoint for bone remineralization through quantitative and qualitative analyses in sequential CT scans. Twenty patients (seven women, 58 ± 8 years) with newly diagnosed MM received standardized induction therapy comprising the anti-SLAMF7 antibody elotuzumab, carfilzomib, lenalidomide, and dexamethasone (E-KRd). All patients underwent whole-body low-dose CT scans before and after six cycles of E-KRd. Two radiologists independently recorded osteolytic lesion sizes, as well as the presence of cortical destruction, pathologic fractures, rim and trabecular sclerosis. Bland-Altman analyses and Krippendorff's α were employed to assess inter-reader reliability, which was high for lesion size measurement (standard error 1.2 mm) and all qualitative criteria assessed (α ≥ 0.74). After six cycles of E-KRd induction, osteolytic lesion size decreased by 22\% (p \&lt; 0.001). While lesion size response did not correlate with the initial lesion size at baseline imaging (Pearson's r = 0.144), logistic regression analysis revealed that the majority of responding osteolyses exhibited trabecular sclerosis (p \&lt; 0.001). The sum of osteolytic lesion sizes on sequential CT scans defines a reliable study endpoint to characterize bone remineralization. Patient level response is strongly associated with the presence of trabecular sclerosis.},
  language  = {en}
}