Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS: Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS: Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.

Investigation of charge ratio variation in mRNA - DEAE-dextran polyplex delivery systems.

mRNA pharmaceuticals represent a new class of therapeutics, with applications, in cancer vaccination, tumour therapy and protein substitution. Formulations are required to deliver messenger RNA (mRNA) to the target sites where induction of genetic transfection following receptor mediated cell uptake & translation is required. In the current study, the cationic polysaccharide diethylaminoethylen (DEAE) - Dextran was selected as a model system carrier for the investigation of polyplex nanoparticle formation together with mRNA as a function of the molar ratio of the components. The structure of the mRNA/Dextran colloids was investigated as a function of the polymer-to-mRNA ratio and correlated with the biological activity determined by cellular transfection with luciferase coding mRNA. Dynamic light scattering (DLS), small angle x-ray scattering (SAXS), and small angle neutron scattering (SANS) with deuterium contrast variation were used to achieve structural insight into the systems. Similarly to previously investigated lipid based systems, colloidally stable particles with confined size were obtained with either excess of positive or negative charge. Highest activity was obtained with positive charge excess. From the scattering experiments information on the internal organization inside the polymer/mRNA systems was derived. Indication for the presence of structural elements in the length scale of ten to 20 nm were found in the excess of dextran, which could be due to either excess or particulate polymer. Information on the molecular organization of the mRNA nanoparticle products may provide a valuable basis for defining critical quality attributes of drug products for pharmaceutical application.