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Different factors, not limited to the lung, influence the progression of ILDs. A "treatable trait" strategy was recently proposed for ILD patients as a precision model of care to improve outcomes. However, no data have been published so far on the prevalence of TTs in ILD. A prospective, observational, cohort study was conducted within the ILD Program at the IRCCS Humanitas Research Hospital (Milan, Italy) between November 2021 and November 2023. TTs were selected according to recent literature and assigned during multidisciplinary discussion (MDD) to one of the following categories: pulmonary, etiological, comorbidities, and lifestyle. Patients were further divided into four groups according to their post-MDD diagnosis: idiopathic ILD, sarcoidosis, connective tissue disease–ILD, and other ILD. The primary study outcome was the prevalence of each TT in the study population. A total of 116 patients with ILD [63.9% male; median (IQR) age: 69 (54–78) years] were included in the study. All the TTs identified in the literature were found in our cohort, except for intractable chronic cough. We also recognized differences in TTs across the ILD groups, with less TTs in patients with sarcoidosis. This analysis provides the first ancillary characterization of TTs in ILD patients in a real setting to date. [ABSTRACT FROM AUTHOR]

Simple Summary: An "intelligent agent" based on deep learning solutions is proposed to detect and non-invasively characterize lung lesions on computed tomography (CT) scans. Our retrospective study aimed to assess the effectiveness of Retina U-Net and the convolutional neural network for computer-aided detection (CADe) and computer-aided diagnosis (CADx) purposes. CADe and CADx were trained, validated, and tested on the publicly available LUNA challenge dataset and two local low-dose CT datasets from the IRCCS Humanitas Research Hospital. (1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools' efficiency; an "intelligent agent" to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnosis (CADx) modules relied on deep learning techniques such as Retina U-Net and the convolutional neural network; (3) Results: Tests were conducted on one publicly available dataset and two local datasets featuring CT scans acquired with different devices to reveal deep learning performances in "real-world" clinical scenarios. The CADe module reached an accuracy rate of 78%, while the CADx's accuracy, specificity, and sensitivity stand at 80%, 73%, and 85.7%, respectively; (4) Conclusions: Two different deep learning techniques have been adapted for CADe and CADx purposes in both publicly available and private CT scan datasets. Experiments have shown adequate performance in both detection and diagnosis tasks. Nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as CNN and Retina U-Net in real-world clinical scenarios, with CT scans from different devices with different sensors' fingerprints and spatial resolution. Continuous reassessment of CADe and CADx's performance is needed during their implementation in clinical practice. [ABSTRACT FROM AUTHOR]

Aims Whether pulmonary artery (PA) dimension and coronary artery calcium (CAC) score, as assessed by chest computed tomography (CT), are associated with myocardial injury in patients with coronavirus disease 2019 (COVID-19) is not known. The aim of this study was to explore the risk factors for myocardial injury and death and to investigate whether myocardial injury has an independent association with all-cause mortality in patients with COVID-19. Methods and Results This is a single-centre cohort study including consecutive patients with laboratory-confirmed COVID-19 undergoing chest CT on admission. Myocardial injury was defined as high-sensitivity troponin I >20 ng/L on admission. A total of 332 patients with a median follow-up of 12 days were included. There were 68 (20.5%) deaths; 123 (37%) patients had myocardial injury. PA diameter was higher in patients with myocardial injury compared with patients without myocardial injury [29.0 (25th–75th percentile, 27–32) mm vs. 27.7 (25–30) mm, P < 0.001). PA diameter was independently associated with an increased risk of myocardial injury [adjusted odds ratio 1.10, 95% confidence interval (CI) 1.02–1.19, P = 0.01] and death [adjusted hazard ratio (HR) 1.09, 95% CI 1.02–1.17, P = 0.01]. Compared with patients without myocardial injury, patients with myocardial injury had a lower prevalence of a CAC score of zero (25% vs. 55%, P < 0.001); however, the CAC score did not emerge as a predictor of myocardial injury by multivariable logistic regression. Myocardial injury was independently associated with an increased risk of death by multivariable Cox regression (adjusted HR 2.25, 95% CI 1.27–3.96, P = 0.005). Older age, lower estimated glomerular filtration rate, and lower PaO2/FiO2 ratio on admission were other independent predictors for both myocardial injury and death. Conclusions An increased PA diameter, as assessed by chest CT, is an independent risk factor for myocardial injury and mortality in patients with COVID-19. Myocardial injury is independently associated with an approximately two-fold increased risk of death. [ABSTRACT FROM AUTHOR]

Focal steatosis and fatty sparing are a frequent finding in liver imaging, and can mimic solid lesions. Liver regional variations in the degree of fat accumulation can be related to vascular anomalies, metabolic disorders, use of certain drugs or coexistence of hepatic masses. CT and MRI are the modalities of choice for the noninvasive diagnosis of hepatic steatosis. Knowledge of CT and MRI appearance of focal steatosis and fatty sparing is crucial for an accurate diagnosis, and to rule-out other pathologic processes. This paper will review the CT and MRI techniques for the diagnosis of hepatic steatosis and the CT and MRI features of common and uncommon causes of focal steatosis and fatty sparing. [ABSTRACT FROM PUBLISHER]