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Objectives Lung ultrasound (LUS) and high-resolution CT (HRCT) are commonly used for the evaluation of interstitial lung disease (ILD). Nintedanib (NIN) is an antifibrotic therapy approved for systemic sclerosis-associated ILD (SSc-ILD). We assessed LUS and quantitative HRCT changes in SSc-ILD patients treated with NIN during a 1 year follow-up, evaluating relationships between imaging variations and functional or quality-of-life outcomes. Methods SSc-ILD patients who started NIN were enrolled and followed for 12 months. Pulmonary function tests and patient-reported outcome measures (PROMs) were assessed half-yearly and quarterly, respectively. LUS was performed quarterly evaluating the presence of B-lines (BL) and pleural line irregularities (PLI). HRCT was repeated after 1 year and quantitatively analysed with CALIPER software. Results Ten patients (70% female, mean age 62 years) were enrolled. The mean total number of both BL and PLI was constantly decreased during NIN treatment, being significantly reduced after 12 months (from 175.1 [66.7] to 120.8 [70.3] for BL, P  = 0.005; and from 50.6 [32.5] to 37.2 [22.4] for PLI, P  = 0.05). Male gender, smoking habit and baseline forced vital capacity <70% predicted were associated with worse LUS outcomes. A greater reduction in both BL and PLI was observed in those who improved in PROMs, especially modified Medical Research Council dyspnoea scale (P  = 0.016 and P  = 0.04, respectively) and Saint George's Respiratory Questionnaire (P  = 0.006 and P  = 0.026, respectively). No significant changes in the CALIPER percentages of normal parenchyma or ILD elements were observed after 12 months of NIN, thus paralleling the stabilization obtained at pulmonary function tests. Conclusion We present preliminary results on NIN effects on SSc-ILD as assessed by LUS, a useful method for frequently repeated monitoring, and CALIPER, a valid implementation whenever a HRCT is performed. [ABSTRACT FROM AUTHOR]

Background/Objectives: The prevalence of Interstitial Lung Disease (ILD) and risk factors for its development in patients with primary Sjögren's syndrome (pSS) are still debated, possibly due to the existence of heterogeneous pSS-related ILD phenotypes. The aims of this study were: 1. To investigate the prevalence and predictive factors for ILD development in a single-center pSS cohort; 2. To characterize different pSS-ILD phenotypes. Methods: Clinical, laboratory and imaging data of pSS patients attending our center from January 2019 to September 2023 were retrospectively analyzed. ILD presence was confirmed on HRCT. Results: Forty-three out of 474 enrolled pSS patients presented ILD (M:F = 6:37), accounting for an overall ILD prevalence of 9.1%. In 19 cases, ILD was the first manifestation of pSS (ILD-onset), while in 24 ILD was diagnosed after pSS (ILD-incident). Compared to ILD-onset, ILD-incident patients more often presented pSS-related hematologic abnormalities (p = 0.012), cutaneous involvement (p = 0.027), inflammatory arthralgias (p = 0.026), C4 hypocomplementemia (p = 0.012) and positive RF (p = 0.031). On the other hand, ILD-onset patients were significantly older at pSS diagnosis (p = 0.008) and presented more severe fibrosis on HRCT (p = 0.008). On the univariate analysis, higher ESSDAI (p = 0.011), Raynaud's phenomenon (p = 0.009), anti-Ro52 (p = 0.031), hypergammaglobulinemia (p = 0.011), Rheumatoid Factor (RF) (p = 0.038) and C4 hypocomplementemia (p = 0.044) at baseline were associated to ILD development during follow-up. On the multivariate analysis, the ESSDAI at baseline (p = 0.05) and Raynaud's phenomenon (p = 0.013) at baseline were the only independent predictors of ILD development. Conclusions: ILD is a relatively common and clinically heterogenous pSS manifestation. Elevated disease activity at pSS onset is a risk factor for ILD development, prompting careful follow-up and intriguingly suggesting that immunomodulatory therapies may prevent ILD. [ABSTRACT FROM AUTHOR]

Simple Summary: We investigated whether the baseline plasma cell-free DNA might help to differentiate malignant nodules from benign lesions observed in screening low-dose computed tomography (LDCT) examinations. Plasma cell-free DNA was determined before the first LDCT in 137 participants in the ITALUNG trial, including 29 with screen-detected malignant nodules (17 prevalent and 12 incident) and 108 with benign nodules. In subjects with prevalent lung cancers (LC), the radiological characteristics well differentiated the malignant nodule, and the cell-free DNA was markedly increased. A total of 75% of subjects with incident LC showed a baseline cell-free DNA ≥ 3.15 ng/mL, compared to 34% of subjects with benign nodules (p = 0.006). Moreover, the cell-free DNA correlated (p = 0.001) with the tumor growth measured with nodular volume doubling time. The baseline plasma cell-free DNA is an independent, potentially useful biomarker in the LC screening process and should be further investigated. The role of total plasma cell-free DNA (cfDNA) in lung cancer (LC) screening with low-dose computed tomography (LDCT) is uncertain. We hypothesized that cfDNA could support differentiation between malignant and benign nodules observed in LDCT. The baseline cfDNA was measured in 137 subjects of the ITALUNG trial, including 29 subjects with screen-detected LC (17 prevalent and 12 incident) and 108 subjects with benign nodules. The predictive capability of baseline cfDNA to differentiate malignant and benign nodules was compared to that of Lung-RADS classification and Brock score at initial LDCT (iLDCT). Subjects with prevalent LC showed both well-discriminating radiological characteristics of the malignant nodule (16 of 17 were classified as Lung-RADS 4) and markedly increased cfDNA (mean 18.8 ng/mL). The mean diameters and Brock scores of malignant nodules at iLDCT in subjects who were diagnosed with incident LC were not different from those of benign nodules. However, 75% (9/12) of subjects with incident LC showed a baseline cfDNA ≥ 3.15 ng/mL, compared to 34% (37/108) of subjects with benign nodules (p = 0.006). Moreover, baseline cfDNA was correlated (p = 0.001) with tumor growth, measured with volume doubling time. In conclusion, increased baseline cfDNA may help to differentiate subjects with malignant and benign nodules at LDCT. [ABSTRACT FROM AUTHOR]

Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder–decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent–Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]