@article{GuggenbergerVogtSongetal.2023,
  author    = {Guggenberger, Konstanze V. and Vogt, Marius L. and Song, Jae W. and Weng, Andreas M. and Fr{\"o}hlich, Matthias and Schmalzing, Marc and Venhoff, Nils and Hillenkamp, Jost and Pham, Mirko and Meckel, Stephan and Bley, Thorsten A.},
  title     = {Intraorbital findings in giant cell arteritis on black blood MRI},
  series = {European Radiology},
  volume    = {33},
  journal   = {European Radiology},
  number    = {4},
  doi       = {10.1007/s00330-022-09256-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324978},
  pages     = {2529-2535},
  year      = {2023},
  abstract  = {Objective Blindness is a feared complication of giant cell arteritis (GCA). However, the spectrum of pathologic orbital imaging findings on magnetic resonance imaging (MRI) in GCA is not well understood. In this study, we assess inflammatory changes of intraorbital structures on black blood MRI (BB-MRI) in patients with GCA compared to age-matched controls. Methods In this multicenter case-control study, 106 subjects underwent BB-MRI. Fifty-six patients with clinically or histologically diagnosed GCA and 50 age-matched controls without clinical or laboratory evidence of vasculitis were included. All individuals were imaged on a 3-T MR scanner with a post-contrast compressed-sensing (CS) T1-weighted sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) BB-MRI sequence. Imaging results were correlated with available clinical symptoms. Results Eighteen of 56 GCA patients (32\%) showed inflammatory changes of at least one of the intraorbital structures. The most common finding was enhancement of at least one of the optic nerve sheaths (N = 13, 72\%). Vessel wall enhancement of the ophthalmic artery was unilateral in 8 and bilateral in 3 patients. Enhancement of the optic nerve was observed in one patient. There was no significant correlation between imaging features of inflammation and clinically reported orbital symptoms (p = 0.10). None of the age-matched control patients showed any inflammatory changes of intraorbital structures. Conclusions BB-MRI revealed inflammatory findings in the orbits in up to 32\% of patients with GCA. Optic nerve sheath enhancement was the most common intraorbital inflammatory change on BB-MRI. MRI findings were independent of clinically reported orbital symptoms. Key Points • Up to 32\% of GCA patients shows signs of inflammation of intraorbital structures on BB-MRI. • Enhancement of the optic nerve sheath is the most common intraorbital finding in GCA patients on BB-MRI. • Features of inflammation of intraorbital structures are independent of clinically reported symptoms.},
  language  = {en}
}
@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}
}