MRI of placenta accreta: diagnostic accuracy and impact of interventional radiology on foetal-maternal delivery outcomes in high-risk women

To assess accuracy and reproducibility of MRI diagnosis of invasive placentation (IP) in high-risk patients and to evaluate reliability of MRI features. Secondary aim was to evaluate impact of interventional radiology (IR) on delivery outcomes in patients with IP at MRI

Imaging-based indices combining disease severity and time from disease onset to predict COVID-19 mortality: A cohort study

Background: COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care. Purpose: The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED). Materials and methods: All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO2) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort. Results: Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUCCT = 0.92, AUCCXR = 0.90, AUCCRP = 0.88, AUCsO2 = 0.88). AUCCXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models. Conclusion: Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation

Modifications of Chest CT Body Composition Parameters at Three and Six Months after Severe COVID-19 Pneumonia: A Retrospective Cohort Study

We aimed to describe body composition changes up to 6-7 months after severe COVID-19 and to evaluate their association with COVID-19 inflammatory burden, described by the integral of the C-reactive protein (CRP) curve. The pectoral muscle area (PMA) and density (PMD), liver-to-spleen (L/S) ratio, and total, visceral, and intermuscular adipose tissue areas (TAT, VAT, and IMAT) were measured at baseline (T0), 2-3 months (T1), and 6-7 months (T2) follow-up CT scans of severe COVID-19 pneumonia survivors. Among the 208 included patients (mean age 65.6 ± 11 years, 31.3% females), decreases in PMA [mean (95%CI) -1.11 (-1.72; -0.51) cm2] and in body fat areas were observed [-3.13 (-10.79; +4.52) cm2 for TAT], larger from T0 to T1 than from T1 to T2. PMD increased only from T1 to T2 [+3.07 (+2.08; +4.06) HU]. Mean decreases were more evident for VAT [-3.55 (-4.94; -2.17) cm2] and steatosis [L/S ratio increase +0.17 (+0.13; +0.20)] than for TAT. In multivariable models adjusted by age, sex, and baseline TAT, increasing the CRP interval was associated with greater PMA reductions, smaller PMD increases, and greater VAT and steatosis decreases, but it was not associated with TAT decreases. In conclusion, muscle loss and fat loss (more apparent in visceral compartments) continue until 6-7 months after COVID-19. The inflammatory burden is associated with skeletal muscle loss and visceral/liver fat loss

Productivity-oriented SLM process parameters effect on the fatigue strength of Inconel 718

The low productivity of the SLM process is known to be a limiting factor, but speeding up the process can lead to material defects. Two sets of SLM process parameters enhancing its productivity by 50 % were devised and tested in comparison with baseline sets, in terms of material microstructure, porosity, surface roughness, static mechanical properties, and HCF behavior, in the as-built and aged conditions. The as-built surface was investigated. Despite a significant increase in the porosity and surface roughness, the fatigue strength was reduced by 5 %. The Murakami areaR parameter effectively correlates the fatigue strength and surface roughness variations

Follow-Up CT Patterns of Residual Lung Abnormalities in Severe COVID-19 Pneumonia Survivors: A Multicenter Retrospective Study

Prior studies variably reported residual chest CT abnormalities after COVID-19. This study evaluates the CT patterns of residual abnormalities in severe COVID-19 pneumonia survivors. All consecutive COVID-19 survivors who received a CT scan 5–7 months after severe pneumonia in two Italian hospitals (Reggio Emilia and Parma) were enrolled. Individual CT findings were retrospectively collected and follow-up CT scans were categorized as: resolution, residual non-fibrotic abnormalities, or residual fibrotic abnormalities according to CT patterns classified following standard definitions and international guidelines. In 225/405 (55.6%) patients, follow-up CT scans were normal or barely normal, whereas in 152/405 (37.5%) and 18/405 (4.4%) patients, non-fibrotic and fibrotic abnormalities were respectively found, and 10/405 (2.5%) had post-ventilatory changes (cicatricial emphysema and bronchiectasis in the anterior regions of upper lobes). Among non-fibrotic changes, either barely visible (n = 110/152) or overt (n = 20/152) ground-glass opacities (GGO), resembling non-fibrotic nonspecific interstitial pneumonia (NSIP) with or without organizing pneumonia features, represented the most common findings. The most frequent fibrotic abnormalities were subpleural reticulation (15/18), traction bronchiectasis (16/18) and GGO (14/18), resembling a fibrotic NSIP pattern. When multiple timepoints were available until 12 months (n = 65), residual abnormalities extension decreased over time. NSIP, more frequently without fibrotic features, represents the most common CT appearance of post-severe COVID-19 pneumonia

Systematic review of existing guidelines for NAFLD assessment

Aim: In this systematic review, guidelines on non-alcoholic fatty liver disease (NAFLD) were evaluated, aiming at a guideline synthesis focusing on diagnosis and staging.Methods: A systematic literature search was conducted on any relevant database or institutional website to find guidelines on NAFLD assessment intended for clinical use on humans, in English, published from January 2010 to August 2020. Included guidelines were appraised using the AGREE II Instrument; those with higher scores and intended for use in adult patients were included in a comparative analysis. Results: Fourteen guidelines were included in the systematic review, eight of which reached an AGREE II score sufficiently high to be recommended for clinical use, of which one developed for pediatric patients only. British and North American guidelines received the highest scores. Most guidelines recommend a screening or case-finding approach in patients with metabolic risk factors who are at increased risk of steatohepatitis or fibrosis. Ultrasound is mostly recommended to confirm steatosis, while the presence of metabolic syndrome, liver function tests, fibrosis scores, and elastographic techniques may help in selecting high-risk patients to be referred to the hepatologist, who may consider liver biopsy, although referral criteria for liver biopsy are not clearly defined. Most guidelines identify the development of noninvasive tests to replace liver biopsy as a research priority.Conclusion: Several high-quality guidelines exist for NAFLD assessment, with no complete agreement on whether to screen high-risk patients and on the tests and biomarkers suggested to stratify patients and select those to be referred to liver biopsy

Systematic review of existing guidelines for NAFLD assessment

Aim: In this systematic review, guidelines on non-alcoholic fatty liver disease (NAFLD) were evaluated, aiming at a guideline synthesis focusing on diagnosis and staging.Methods: A systematic literature search was conducted on any relevant database or institutional website to find guidelines on NAFLD assessment intended for clinical use on humans, in English, published from January 2010 to August 2020. Included guidelines were appraised using the AGREE II Instrument; those with higher scores and intended for use in adult patients were included in a comparative analysis. Results: Fourteen guidelines were included in the systematic review, eight of which reached an AGREE II score sufficiently high to be recommended for clinical use, of which one developed for pediatric patients only. British and North American guidelines received the highest scores. Most guidelines recommend a screening or case-finding approach in patients with metabolic risk factors who are at increased risk of steatohepatitis or fibrosis. Ultrasound is mostly recommended to confirm steatosis, while the presence of metabolic syndrome, liver function tests, fibrosis scores, and elastographic techniques may help in selecting high-risk patients to be referred to the hepatologist, who may consider liver biopsy, although referral criteria for liver biopsy are not clearly defined. Most guidelines identify the development of noninvasive tests to replace liver biopsy as a research priority.Conclusion: Several high-quality guidelines exist for NAFLD assessment, with no complete agreement on whether to screen high-risk patients and on the tests and biomarkers suggested to stratify patients and select those to be referred to liver biopsy

Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features

Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction