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Background: To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities’ persistence were investigated. Methods: Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12. Results: The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12. Conclusions: CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities’ persistence at T12.

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55–69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database “AI for radiology” was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.

Background: Chest computed tomography (CT) is the gold standard for the evaluation of systemic sclerosis-related interstitial lung disease (SSc-ILD). Lung ultrasound (LUS) is a radiation-free tool that identifies the B-lines as a main feature of ILD. We aimed to investigate the role of LUS in the evaluation of the extent of SSc-ILD. Methods: Adult SSc patients underwent pulmonary function tests (PFTs), LUS and CT. The CT images were qualitatively, semi-quantitatively (the Wells score on five levels and the categorical Goh et al. staging) and quantitatively (histogram-based densitometry) analysed for ILD. LUS quantified B-lines in 21 intercostal spaces on both the anterior and posterior chest wall. Results: Out of the 77 SSc patients eligible for the study, 35 presented with ILD on CT (21 limited, 14 extensive). Total B-lines significantly differentiated ILD vs. no ILD (median 24 vs. 8, p < 0.001). Posterior and total B-lines significantly differentiated limited from absent ILD, while anterior B-lines distinguished extensive from limited ILD. Total B-lines correlated with the Wells score (r = 0.446, p < 0.001) and MLA (r = −0.571, p < 0.001); similar results were confirmed when anterior and posterior B-lines were analysed separately. Conclusions: LUS is a useful tool to identify SSc-ILD and to correlate with different evaluations of ILD extent and severity.

In recent years, many articles have demonstrated that magnetic resonance imaging (MRI) may be performed successfully in the study of the chest. The aim of this study was to evaluate the potential role of MRI in the differentiation of benign from malignant pleural disease with a special focus on malignant pleural mesothelioma and on MRI protocols. A systematic literature search was performed to find original articles about chest MRI in patients with either benign or malignant pleural disease. We retrieved 1246 papers and 17 studies were finally identified as being in accordance with our purpose. For a morphologic assessment, T1-weighted and T2-weighted sequences were usually performed, eventually associated with T1 post-contrast sequences for better detection of pleural lesions. Functional sequences such as Diffusion Weighting Imaging (DWI), associated with the evaluation of Apparent Diffusion Coefficient (ADC) maps, were lately and gradually introduced in chest MRI protocols and their potentiality in differentiating benign from malignant disease has been investigated by many authors. Many progresses have been performed to improve quality images and diagnostic performances of MRI. A better and early identification of pleural disease may be obtained, providing MRI as a possible tool that can differentiate malignant from benign pleural disease without using invasive procedures.