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Background: D-dimer testing is known to have a high sensitivity at simultaneously low specificity, resulting in nonspecific elevations in a variety of conditions.Methods: This retrospective study sought to assess diagnostic and prognostic features of D-dimers in cancer patients referred to the emergency department for suspected pulmonary embolism (PE) and deep vein thrombosis (DVT). In total, 526 patients with a final adjudicated diagnosis of PE (n = 83) and DVT (n = 69) were enrolled, whereas 374 patients served as the comparative group, in which venous thromboembolism (VTE) has been excluded. Results:For the identification of VTE, D-dimers yielded the highest positive predictive value of 96% (95% confidence interval (CI), 85-99) at concentrations of 9.9 mg/L and a negative predictive value of 100% at .6 mg/L (95% CI, 97-100).At the established rule-out cut-off level of .5 mg/L, D-dimers were found to be very sensitive (100%) at a moderate specificity of nearly 65%. Using an optimised cut-off value of 4.9 mg/L increased the specificity to 95% for the detection of lifethreatening VTE at the cost of moderate sensitivities (64%). During a median follow-up of 30 months, D-dimers positively correlated with the reoccurrence of VTE (p = .0299) and mortality in both cancer patients with VTE (p < .0001) and without VTE (p = .0008). Conclusions:Although D-dimer testing in cancer patients is discouraged by current guidelines, very high concentrations above the 10-fold upper reference limit contain diagnostic and prognostic information and might be helpful in risk assessment, while low concentrations remain useful for ruling out VTE.

Objectives : To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data. Methods : We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into subcohorts with or without necessity of intensive care unit (ICU) treatment. Subcohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test. Results : We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all P<0.001 if not otherwise stated) with occurrence of ICU stay (R=0.74), length of ICU stay (R=0.81), lethal outcome (R=0.56) and length of hospital stay (R=0.33, P<0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R=0.60), lactate dehydrogenase (LDH) (R=0.60), troponin (TNTHS) (R=0.55) and c-reactive protein (CRP) (R=0.51). Differences (P<0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher Opacityscore (12.50 vs. 4.96) and severity of laboratory data changes such as CRP (11.64 vs. 5.07 mg/dl, P<0.01). Conclusions : Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.

Background The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. Methods In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student’s t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. Results Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955–1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767–0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587–0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10–44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697–0.864], P = .01). Conclusions Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.