Objective: The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. Methods: MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. Results: The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. Conclusion: This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding.

Objective: Body tissue composition plays a crucial role in the multisystemic processes of advanced liver disease and has been shown to be influenced by transjugular intrahepatic portosystemic shunt (TIPS). A differentiated analysis of the various tissue compartments has not been performed until now. The purpose of this study was to evaluate the value of imaging biomarkers derived from automated body composition analysis (BCA) to predict clinical and functional outcome. Methods: A retrospective analysis of 56 patients undergoing TIPS procedure between 2013 and 2021 was performed. BCA on the base of pre-interventional CT examination was used to determine quantitative data as well as ratios of bone, muscle and fat masses. Furthermore, a BCA-derived sarcopenia marker was investigated. Regarding potential correlations between BCA imaging biomarkers and the occurrence of hepatic encephalopathy (HE) as well as 1-year survival, an exploratory analysis was conducted. Results: No BCA imaging biomarker was associated with the occurrence of HE after TIPS placement. However, there were significant differences in alive and deceased patients regarding the BCA-derived sarcopenia marker (alive: 1.60, deceased: 1.83, p = 0.046), ratios of intra- and intermuscular fat/skeletal volume (alive: 0.53, deceased: 0.31, p = 0.015) and intra- and intermuscular fat/muscle volume (alive: 0.21, deceased: 0.14, p = 0.031). Conclusion: A lower amount of intra- and intermuscular adipose tissue might have protective effects regarding liver derived complications and survival. Advances in knowledge: Precise characterization of body tissue components with automated BCA might provide prognostic information in patients with advanced liver disease undergoing TIPS procedure.

<b><i>Introduction:</i></b> An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in consumption, the storage of platelet concentrates (PCs) becomes challenging. This emphasizes the need for reliable prediction of needed PCs for the blood bank inventories. Therefore, the objective of this study was to evaluate multimodal data from multiple source systems within a hospital to predict the number of platelet transfusions in 3 days on a per-patient level. <b><i>Methods:</i></b> Data were collected from 25,190 (42% female and 58% male) patients between 2017 and 2021. For each patient, the number of received PCs, platelet count blood tests, drugs causing thrombocytopenia, acute platelet diseases, procedures, age, gender, and the period of a patient’s hospital stay were collected. Two models were trained on samples using a sliding window of 7 days as input and a day 3 target. The model predicts whether a patient will be transfused 3 days in the future. The model was trained with an excessive hyperparameter search using patient-level repeated 5-fold cross-validation to optimize the average macro F2-score. <b><i>Results:</i></b> The trained models were tested on 5,022 unique patients. The best-performing model has a specificity of 0.99, a sensitivity of 0.37, an area under the precision-recall curve score of 0.45, an MCC score of 0.43, and an F1-score of 0.43. However, the model does not generalize well for cases when the need for a platelet transfusion is recognized. <b><i>Conclusion:</i></b> A patient AI-based platelet forecast could improve logistics management and reduce blood product waste. In this study, we build the first model to predict patient individual platelet demand. To the best of our knowledge, we are the first to introduce this approach. Our model predicts the need for platelet units for 3 days in the future. While sensitivity underperforms, specificity performs reliably. The model may be of clinical use as a pretest for potential patients needing a platelet transfusion within the next 3 days. As sensitivity needs to be improved, further studies should introduce deep learning and wider patient characterization to the methodological multimodal, multisource data approach. Furthermore, a hospital-wide consumption of PCs could be derived from individual predictions.