The localization and biological features of LM from NET defines therapeutic management and is predictive of outcome.

Objectives Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Methods Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. Results The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Conclusions Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. Key Points • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Patients with locally advanced disease, managed by a multidisciplinary treatment strategy, achieved a similar long-term survival to patients with early disease (stadium I + II).

Background Invasive fungal infections (IFI) are major risks for mortality after liver transplantation (LT). The aim of this study was to evaluate possible risk factors for the development of IFI after LT. Material/Methods All adult patients with IFI after LT between January 2012 and December 2016 at Essen University were identified. Pre-, intra-, and postoperative data were reviewed. These were compared to a 1-to-3 matched control group. Multinominal univariate and multivariate regression analyses were performed. Results Out of the 579 adults who underwent LT, 33 (5.6%) developed postoperative IFI. Fourteen had invasive aspergillosis with 7 (50%) mortality, and 19 had Candida sepsis with 7 (37%) mortality. The overall mortality due to invasive fungal infections was 42%. Perfusion fluid contamination with yeast was detected in 5 patients (15%). Multivariate regression analyses showed that preoperative dialysis (OR=1.163; CI: 1.038–1.302), Eurotransplant donor risk index (OR=0.04; CI=0.003–0.519), length of hospital stay (OR=25.074; CI: 23.99–26.208), and yeast contamination of the preservation fluid (OR=47.8; CI: 4.77–478, 96) were associated with IFI in the Candida group, whereas duration of surgery (OR=1.013; CI: 1.005–1.022), ventilation hours (OR=0.993; CI=0.986–0.999), and days of postoperative dialysis (OR=1.195; CI: 1.048–1,362) were associated with IFI in the aspergillosis group. Conclusions Post-LT IFI had 42% mortality in our cohort . Prophylactic antifungal therapy should be expanded to broader risk groups as defined above.

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