The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp. The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

Follow-Up Study of the Chest CT Characteristics of COVID-19 Survivors Seven Months After Recovery

Background: It has remained a concern whether any long-term pulmonary sequelae exist for COVID-19 survivors.Methods: Forty-one patients (22 men and 19 women, 50 ± 14 years) confirmed with COVID-19 performed follow-up chest CT and cardiopulmonary exercise testing at 7 months after discharge. Patients were divided into fibrosis group and non-fibrosis group according to the evidence of fibrosis on follow-up CT. The clinical data and the CT findings were recorded and analyzed.Results: The predominant CT patterns of abnormalities observed at 7 months after discharge were parenchymal band (41%), interlobular septal thickening (32%), and traction bronchiectasis (29%). Sixty-one percent of the patients achieved complete radiological resolution, and 29% of patients developed pulmonary fibrosis. Compared with the patients in the non-fibrosis group, the patients in the fibrosis group were older, with a longer hospital stay, a higher rate of steroid and mechanical ventilation therapy, lower levels of lymphocyte and T cell count, higher levels of D-dimer and lactic dehydrogenase, and higher quantitative CT parameters (opacity score, volume of opacity, and percentage of opacity) at discharge. Besides, oxygen consumption and metabolic equations were decreased and ventilatory equivalent for carbon dioxide was increased in patients in the fibrosis group. Logistic regression analyses revealed that age, steroid therapy, presence of traction bronchiectasis on chest CT at discharge, and opacity score at discharge, were independent risk factors for developing pulmonary fibrosis at 7 months after discharge. Receiver operating characteristic analysis revealed that the combined clinical-radiological model was better than the clinical-only model in the prediction of pulmonary fibrosis.Conclusions: The chest CT lesions could be absorbed without any sequelae for most patients with COVID-19, whereas older patients with severe conditions are more prone to develop fibrosis, which may further lead to cardiopulmonary insufficiency. The combined clinical-radiological model may predict the formation of pulmonary fibrosis early.

Radiological Modelling of Thermaikos Gulf

The radiological model of Thermaikos Gulf ecosystem has been designed based on the MOIRA-PLUS Decision Support System properly modified for the marine environment. Radioactive fallout contamination exercises have been performed for 90Sr and 137Cs radioisotopes, within the frame of environmental sensitivity analysis. The model’s performance has been calibrated, taking into account the available 137Cs deposition estimations and published experimental concentrations to the sediment, the water and the fish at the Gulf, from the time of the Chernobyl accident up to now. The radiation doses to adults – assuming that their entire food intake from the marine pathway comes from the local environment – after the first year of one instantaneous deposition of 1000 Bq/m2, were found 0.72 μSv for 137Cs and 8.8 μSv for 90Sr, respectively. The results are consistent with other models estimations in Northern Seas and NE Aegean Sea.