Cardiac output and pulmonary vascular resistance (PVR) were measured in 19 patients by means of catheterization of the right side of the heart. Results were compared with the cardiac output and indexes of pulmonary arterial blood flow estimated with velocity-encoded magnetic resonance (MR) imaging. Correlations were good between estimates with right-sided heart catheterization and those with velocity-encoded MR imaging. By providing accurate pulmonary arterial blood flow measurements, velocity-encoded MR imaging allowed distinction of patients with high PVR from subjects with normal PVR.

Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using Chest CT for COVID-19, results of chest CT and RT-PCR were compared together and with the final discharge diagnosis used as reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2 and April 24 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographical prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having a COVID-19 pneumonia who underwent within 48 hours both initial chest CT and at least one RT-PCR testing were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using Mann-Whitney Test, Student’s t-test, Chi-square test and Pearson’s correlation. A p value <.05 determined statistical significance. Results Twenty-six of 26 hospital radiology departments responded to the survey with 7500 patients entered; 2652 did not have RT-PCR results or had unknown or excess delay between RT-PCR and CT. After exclusions, 4824 patients (mean age 64, ± 19 yrs, 2669 males) were included. Using final diagnosis as the reference, 2564 of the 4824 patients were positive for COVID-19 (53%). Sensitivity, specificity, NPV and PPV of chest CT for diagnosing COVID-19 were 2319/2564 (90%, 95% confidence interval [CI]: 89, 91), 2056/2260 (91%, 95%CI: 91, 92%), 2056/2300 (89%, 95%CI; 87, 90%) and 2319/2524 (92%, 95%CI 91, 93%) respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of suspected COVID-19 patients was successful.

Purpose To determine the impact of the COVID-19 on the CT activities in French radiological centers during the epidemic peak. Materials and methods A cross-sectional prospective CT scan survey was conducted between March 16 and April 12, 2020, in accordance with the local IRB. Seven hundred nine radiology centers were invited to participate in a weekly online survey. Numbers of CT examinations related to COVID-19 including at least chest (CT covid) and whole chest CT scan activities (CT chest) were recorded each week. A sub-analysis on French departments was performed during the 4 weeks of the study. The impact of the number of RT-PCRs (reverse transcriptase polymerase chain reactions) on the CT workflow was tested using two-sample t test and Pearson's test. Results Five hundred seventy-seven structures finally registered (78%) with mean response numbers of 336 ± 18.9 (323; 351). Mean CT chest activity per radiologic structure ranged from 75.8 ± 133 (0-1444) on week 12 to 99.3 ± 138.6 (0-1147) on week 13. Mean ratio of CT covid on CT chest varied from 0.36 to 0.59 on week 12 and week 14 respectively. There was a significant relationship between the number of RT-PCR performed and the number of CT covid (r = 0.73, p = 3.10 −16) but no link with the number of positive RT-PCR results. Conclusion In case of local high density COVID-19, CT workflow is strongly modified and redirected to the management of these specific patients. Key Points • Over the 4-week survey period, 117,686 chest CT (CT total) were performed among the responding centers, including 61,784 (52%) CT performed for COVID-19 (CT covid). • Across the country, the ratio CT covid /CT total varied from 0.36 to 0.59 and depended significantly on the local epidemic density (p = 0.003). • In clinical practice, in a context of growing epidemic, in France, chest CT was used as a surrogate to RT-PCR for patient triage.

The proposed approach provides a promising solution for an effective and accurate prostate radiotherapy treatment planning since it satisfies the desired clinical accuracy.

Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6–8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan–Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.