Baseline Cell-Free DNA Can Predict Malignancy of Nodules Observed in the ITALUNG Screening Trial.

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.

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated. RESULTS: Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively). CONCLUSION: Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies.

A case report of unusual recurrent bronchopneumonia infections in Mounier-Kuhn syndrome.

Mounier-Kuhn syndrome is a rare airway disease characterized by tracheal and bronchial dilatation, primarily affecting middle-aged men. We present a case of Mounier-Kuhn syndrome in a 40-year-old man with a history of recurrent respiratory infections since adolescence. The diagnostic journey involved a multidisciplinary approach incorporating clinical evaluation, radiological imaging, and bronchoscopy. Computed tomography findings, including maximum intensity projection reconstructions and 3D rendering, facilitated the diagnosis by revealing significant airway dilation and associated abnormalities. Treatment primarily focused on supportive measures, including antibiotic therapy and respiratory physiotherapy. This case underscores the importance of considering Mounier-Kuhn syndrome in patients with recurrent respiratory infections and highlights the role of advanced imaging techniques in diagnosis.

Blood oxygenation state in COVID-19 patients: Unexplored role of 2,3-bisphosphoglycerate.

BACKGROUND: COVID-19 reduces lung functionality causing a decrease of blood oxygen levels (hypoxemia) often related to a decreased cellular oxygenation (hypoxia). Besides lung injury, other factors are implicated in the regulation of oxygen availability such as pH, partial arterial carbon dioxide tension (PaCO2), temperature, and erythrocytic 2,3-bisphosphoglycerate (2,3-BPG) levels, all factors affecting hemoglobin saturation curve. However, few data are currently available regarding the 2,3-BPG modulation in SARS-CoV-2 affected patients at the hospital admission. MATERIAL AND METHODS: Sixty-eight COVID-19 patients were enrolled at hospital admission. The lung involvement was quantified using chest-Computer Tomography (CT) analysed with automatic software (CALIPER). Haemoglobin concentrations, glycemia, and routine analysis were evaluated in the whole blood, while partial arterial oxygen tension (PaO2), PaCO2, pH, and HCO3- were assessed by arterial blood gas analysis. 2,3-BPG levels were assessed by specific immunoenzymatic assays in RBCs. RESULTS: A higher percentage of interstitial lung disease (ILD) and vascular pulmonary-related structure (VRS) volume on chest-CT quantified with CALIPER had been found in COVID-19 patients with a worse disease outcome (R = 0.4342; and R = 0.3641, respectively). Furthermore, patients with lower PaO2 showed an imbalanced acid-base equilibrium (pH, p = 0.0208; PaCO2, p = 0.0496) and a higher 2,3-BPG levels (p = 0.0221). The 2,3-BPG levels were also lower in patients with metabolic alkalosis (p = 0.0012 vs. no alkalosis; and p = 0.0383 vs. respiratory alkalosis). CONCLUSIONS: Overall, the data reveal a different pattern of activation of blood oxygenation compensatory mechanisms reflecting a different course of the COVID-19 disease specifically focusing on 2,3-BPG modulation.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.

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.

Mortality surrogates in combined pulmonary fibrosis and emphysema.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) with coexistent emphysema, termed combined pulmonary fibrosis and emphysema (CPFE) may associate with reduced forced vital capacity (FVC) declines compared to non-CPFE IPF patients. We examined associations between mortality and functional measures of disease progression in two IPF cohorts. METHODS: Visual emphysema presence (>0% emphysema) scored on computed tomography identified CPFE patients (CPFE/non-CPFE: derivation cohort n=317/n=183, replication cohort n=358/n=152), who were subgrouped using 10% or 15% visual emphysema thresholds, and an unsupervised machine-learning model considering emphysema and interstitial lung disease extents. Baseline characteristics, 1-year relative FVC and diffusing capacity of the lung for carbon monoxide (D LCO) decline (linear mixed-effects models), and their associations with mortality (multivariable Cox regression models) were compared across non-CPFE and CPFE subgroups. RESULTS: In both IPF cohorts, CPFE patients with ≥10% emphysema had a greater smoking history and lower baseline D LCO compared to CPFE patients with <10% emphysema. Using multivariable Cox regression analyses in patients with ≥10% emphysema, 1-year D LCO decline showed stronger mortality associations than 1-year FVC decline. Results were maintained in patients suitable for therapeutic IPF trials and in subjects subgrouped by ≥15% emphysema and using unsupervised machine learning. Importantly, the unsupervised machine-learning approach identified CPFE patients in whom FVC decline did not associate strongly with mortality. In non-CPFE IPF patients, 1-year FVC declines ≥5% and ≥10% showed strong mortality associations. CONCLUSION: When assessing disease progression in IPF, D LCO decline should be considered in patients with ≥10% emphysema and a ≥5% 1-year relative FVC decline threshold considered in non-CPFE IPF patients.

Lung ultrasound and high-resolution computed tomography quantitative variations during nintedanib treatment for systemic sclerosis-associated interstitial lung disease.

OBJECTIVES: Lung ultrasound (LUS) and high-resolution computed tomography (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 one-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 twelve 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 one 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 twelve months (from 175.1 ± 66.7-120.8 ± 70.3 for BL, p= 0.005 and from 50.6 ± 32.5-37.2 ± 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 of 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 twelve months of NIN, thus paralleling the stabilization obtained at pulmonary function tests. CONCLUSIONS: 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.

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.

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.

Real life data on nintedanib safety: idiopathic pulmonary fibrosis versus systemic sclerosis-interstitial lung disease and strategies adopted to manage adverse effects.

OBJECTIVE: Nintedanib (NIN) is an antifibrotic drug approved to slow the progression of idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-related interstitial lung disease (SSc-ILD). NIN can frequently cause gastrointestinal adverse effects. We aimed to investigate the NIN safety profile in a real life setting, comparing IPF and SSc-ILD patients and evaluating the strategies adopted to manage NIN adverse effects. METHODS: Patients taking NIN for IPF or SSc-ILD were enrolled. Alongside epidemiological and disease-specific data, the period of NIN use and the need for dosage reduction and/or interruption were investigated. Particular attention was paid to possible adverse effects and strategies adopted to manage them. RESULTS: Twenty-seven SSc-ILD and 82 IPF patients were enrolled. No significant differences emerged between the two cohorts regarding the frequency of any possible adverse effect. Although the rates of NIN dosage reduction or interruption were similar between the two subgroups, SSc-ILD presented a mean period before NIN dosage reduction and NIN interruption significantly shorter than IPF (3 ± 2.6 vs 10.5 ± 8.9 months-p < 0.001 and 2.3 ± 0.5 vs 10.3 ± 9.9 months-p = 0.008, respectively). Several different strategies were tried to manage NIN adverse effects: especially in SSc-ILD, the variable combination of diet adjustment set by a nutritionist, probiotics and diosmectite was ultimately successful in maintaining patients on an adequate dose of NIN. CONCLUSION: We presented data on the NIN safety profile in a real life setting, which was similar between SSc-ILD and IPF. A combination of multiple managing strategies and dose adjustment appears essential to cope optimally with NIN adverse effects.

Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports.

Background: Structured reporting has been demonstrated to increase report completeness and to reduce error rate, also enabling data mining of radiological reports. Still, structured reporting is perceived by radiologists as a fragmented reporting style, limiting their freedom of expression. Purpose: A deep learning-based natural language processing method was developed to automatically convert unstructured COVID-19 chest CT reports into structured reports. Methods: Two hundred-two COVID-19 chest CT were retrospectively reviewed by two experienced radiologists, who wrote for each exam a free-form text radiological report and coherently filled the template provided by the Italian Society of Medical and Interventional Radiology, used as ground-truth. A semi-supervised convolutional neural network was implemented to extract 62 categorical variables from the report. Two iterations were carried-out, the first without fine-tuning, the second one performing a fine-tuning. The performance was measured using the mean accuracy and the F1 mean score. An error analysis was performed to identify errors entirely attributable to incorrect processing of the model. Results: The algorithm achieved a mean accuracy of 93.7% and an F1 score 93.8% in the first iteration. Most of the errors were exclusively attributable to wrong inference (46%). In the second iteration the model achieved for both parameters 95,8% and percentage of errors attributable to wrong inference decreased to 26%. Conclusions: The convolutional neural network achieved an optimal performance in the automated conversion of free-form text into structured radiological reports, overcoming all the limitation attributed to structured reporting and finally paving the way for data mining of radiological report.

Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed.

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.

Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.

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.

The use of continuous positive airway pressure during the second and third waves of the COVID-19 pandemic.

Background: In a preliminary study during the first COVID-19 pandemic wave, we reported a high rate of success with continuous positive airway pressure (CPAP) in preventing death and invasive mechanical ventilation (IMV). That study, however, was too small to identify risk factors for mortality, barotrauma and impact on subsequent IMV. Thus, we re-evaluated the efficacy of the same CPAP protocol in a larger series of patients during second and third pandemic waves. Methods: 281 COVID-19 patients with moderate-to-severe acute hypoxaemic respiratory failure (158 full-code and 123 do-not-intubate (DNI)), were managed with high-flow CPAP early in their hospitalisation. IMV was considered after 4 days of unsuccessful CPAP. Results: The overall recovery rate from respiratory failure was 50% in the DNI and 89% in the full-code group. Among the latter, 71% recovered with CPAP-only, 3% died under CPAP and 26% were intubated after a median CPAP time of 7 days (IQR: 5-12 days). Of the patients who were intubated, 68% recovered and were discharged from the hospital within 28 days. Barotrauma occurred during CPAP in <4% of patients. Age (OR 1.128; p <0.001) and tomographic severity score (OR 1.139; p=0.006) were the only independent predictors of mortality. Conclusions: Early treatment with CPAP is a safe option for patients with acute hypoxaemic respiratory failure due to COVID-19.

Pulmonary emphysema and coronary artery calcifications at baseline LDCT and long-term mortality in smokers and former smokers of the ITALUNG screening trial.

OBJECTIVES: Cardiovascular disease (CVD), lung cancer (LC), and respiratory diseases are main causes of death in smokers and former smokers undergoing low-dose computed tomography (LDCT) for LC screening. We assessed whether quantification of pulmonary emphysematous changes at baseline LDCT has a predictive value concerning long-term mortality. METHODS: In this longitudinal study, we assessed pulmonary emphysematous changes with densitometry (volume corrected relative area below - 950 Hounsfield units) and coronary artery calcifications (CAC) with a 0-3 visual scale in baseline LDCT of 524 participants in the ITALUNG trial and analyzed their association with mortality after 13.6 years of follow-up using conventional statistics and a machine learning approach. RESULTS: Pulmonary emphysematous changes were present in 32.3% of subjects and were mild (6% ≤ RA950 ≤ 9%) in 14.9% and moderate-severe (RA950 > 9%) in 17.4%. CAC were present in 67% of subjects (mild in 34.7%, moderate-severe in 32.2%). In the follow-up, 81 (15.4%) subjects died (20 of LC, 28 of other cancers, 15 of CVD, 4 of respiratory disease, and 14 of other conditions). After adjusting for age, sex, smoking history, and CAC, moderate-severe emphysema was significantly associated with overall (OR 2.22; 95CI 1.34-3.70) and CVD (OR 3.66; 95CI 1.21-11.04) mortality. Machine learning showed that RA950 was the best single feature predictive of overall and CVD mortality. CONCLUSIONS: Moderate-severe pulmonary emphysematous changes are an independent predictor of long-term overall and CVD mortality in subjects participating in LC screening and should be incorporated in the post-test calculation of the individual mortality risk profile. KEY POINTS: • Densitometry allows quantification of pulmonary emphysematous changes in low-dose CT examinations for lung cancer screening. • Emphysematous lung density changes are an independent predictor of long-term overall and cardio-vascular disease mortality in smokers and former smokers undergoing screening. • Emphysematous changes quantification should be included in the post-test calculation of the individual mortality risk profile.

Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: A prospective comparative study with chest computed tomography.

During COVID-19 pandemic, lung ultrasound (LUS) proved to be of great value in the diagnosis and monitoring of patients with pneumonia. However, limited data exist regarding its use to assess aeration changes during follow-up (FU). Our study aims to prospectively evaluate 232 subjects who underwent a 3-month-FU program after hospitalization for COVID-19 at the University Hospital of Pisa. The goals were to assess the usefulness of standardized LUS compared with the gold standard chest computed tomography (CT) to evaluate aeration changes and to verify LUS and CT agreement at FU. Patients underwent in the same day a standardized 16-areas LUS and high-resolution chest CT reported by expert radiologists, assigning interpretative codes. Based on observations distribution, LUS score cut-offs of 3 and 7 were selected, corresponding to the 50th and 75th percentile, respectively. Patients with LUS scores above both these thresholds were older and with longer hospital stay. Patients with a LUS score ≥3 had more comorbidities. LUS and chest CT showed a high agreement in identifying residual pathological findings, using both cut-off scores of 3 (OR 14,7; CL 3,6-64,5, Sensitivity 91%, Specificity 49%) and 7 (OR 5,8; CL 2,3-14,3, Sensitivity 65%, Specificity 79%). Our data suggest that LUS is very sensitive in identifying pathological findings at FU after a hospitalization for COVID-19 pneumonia, compared to CT. Given its low cost and safety, LUS could replace CT in selected cases, such as in contexts with limited resources or it could be used as a gate-keeper examination before more advanced techniques.

Glandular involvement in primary Sjögren's syndrome patients with interstitial lung disease-onset and sicca-onset, a single centre cross-sectional study.

OBJECTIVES: Primary Sjögren's syndrome (pSS) is an autoimmune exocrinopathy classically presenting with sicca symptoms. Nonetheless, disease onset with extraglandular manifestations, including interstitial lung disease (ILD), is increasingly reported. However, studies investigating pSS patients presenting with ILD (pSS-ILD) are limited.Aim of this study was to better characterise the phenotype of pSS patients presenting with ILD in comparison to pSS patients with classicsicca-onset. We especially investigated whether the two groups differed in glandular involvement comparing functional, imaging andhistologic findings, as well as patient reported outcome (PRO). METHODS: Consecutive newly diagnosed pSS patients, all fulfilling the ACR/EULAR 2016 criteria, were included in this cross-sectional study from September 2016 to October 2021. Presence of ILD at pSS diagnosis was defined based on clinical findings, imaging assessment and pulmonary function tests (PFT). In addition to functional tests, a minor salivary gland biopsy was performed in all cases, recording number of foci, focus score (FS) and GC-like structures. Salivary glands ultrasonography (SGUS) was graded using the OMERACT semiquantitative scoring system (0-3) based on parenchyma inhomogeneity. PRO including ESSPRI, OHIP and OSSDI were collected.Extraglandular clinical features and biological abnormalities included in the ESSDAI were recorded. Data were expressed as mean±SD for continuous variables and as absolute frequencies and percentages for categorical variables. Chi-Square test and Mann-Whitney U-test and ANOVA were performed for comparisons of categorical variables and continuous variables, respectively. RESULTS: We included 178 newly diagnosed pSS patients (F:M=158:20). ILD was the first pSS manifestation in 11 (6%) cases, 8 F and 3 M, with a median time from ILD onset to pSS diagnosis of 2 years (25-75 IQ 1-4.5). Of the 11 pSS-ILD patients, HRCT pattern was defined as NSIP in 4, UIP in 4, NSIP+OP in 2 and LIP in 1 patient. Dyspnoea on exertion or chronic cough were reported by 7/11 (63.6%) patients.In comparison to sicca-onset patients, pSS-ILD patients presented an older age at diagnosis (55±13 vs. 70±7, p= 0.001) and a higher ESSDAI (3.9±4.7 vs. 12.3±4.3, p=0.001), driven by the pulmonary domain. Regarding glandular involvement, pSS-ILD patients reported milder xeropthalmia (VAS 5.8±3.1 vs. 2.8±3.5, p=0.002) and significative lower scores in OSDI (35.6±24.9 vs. 15.3±22.9, p=0.04) and OHIP (4.8±4.4 vs. 1.4±3.8, p=0.04), despite no significant differences observed between the two groups in ocular tests and unstimulated salivary flow rate. With respect to histology, no significant differences were found in number of foci, FS and GC-like structures. We observed a significantly different distribution of the SGUS OMERACT score in the two groups: none of pSS-ILD patients presented a SGUS OMERACT score ≥2 in the submandibular glands (SG), in contrast to 41/132 (31.1%) of the patients in the classical sicca-onset group (p=0.03). Finally, no significant differences were observed between the two groups with respect to non-pulmonary extraglandular manifestations, serologic features and other biological parameters. CONCLUSIONS: ILD-onset pSS patients represent an atypical phenotypic subset, with less pronounced sialadenitis structural changes in salivary glands, and with sicca symptoms probably overshadowed by the respiratory disease.

Lung Ultrasound B-Lines in the Evaluation of the Extent of Interstitial Lung Disease in Systemic Sclerosis.

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.

Quantitative texture-based analysis of pulmonary parenchymal features on chest CT: comparison with densitometric indices and short-term effect of changes in smoking habit.

PURPOSE: To investigate the correlations between densitometric and Computer Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER)-derived indices of pulmonary emphysema and their change in the short-term period for groups of patients with different smoking habits. METHOD: This retrospective study included 284 subjects from the ITALUNG trial (198 men and 86 women; mean±sd age 60±4 years) who underwent low-dose chest computed tomography at baseline and 2-year follow-up. Subjects were divided into four groups (persistent smokers, restarters, quitters and former smokers) according to their smoking habit at baseline and follow-up. Densitometric and texture analyses were performed, using CALIPER software. A correlation analysis was conducted between CALIPER-derived low-attenuation areas (LAAs) and densitometric indices, including the 15th percentile of the whole-lung attenuation histogram (Perc15) and the relative areas with density ≤-950 HU (RA950). Densitometric indices and LAAs were evaluated at baseline and variation assessed longitudinally with comparisons between groups with different smoking habit. Further analysis of parenchymal changes per pulmonary zone was performed. RESULTS: LAAs were strongly correlated with Perc15 (rs=0.81; p<0.001) and RA950 (rs=0.905; p<0.001). At baseline, the group of smokers showed higher Perc15, lower RA950, lower LAAs (particularly mild sub-class of LAAs) than the group of ex-smokers (p<0.001). At 2-year follow-up, densitometric indices and LAAs increased in persistent smokers, former smokers and quitters (p<0.05). The progression was larger and statistically more significant in quitters (p<0.001). CONCLUSION: CALIPER texture analysis provides an objective measure comparable to traditional density/histogram features to assess the lung parenchymal changes in relation to different smoking habits.

Evaluation of Interstitial Lung Disease in Idiopathic Inflammatory Myopathies Through Semiquantitative and Quantitative Analysis of Lung Computed Tomography.

PURPOSE: To perform a semiquantitative and quantitative analysis of interstitial lung disease (ILD), through computed tomography (CT), in different serological subgroups of idiopathic inflammatory myopathies (IIM) patients, to find radiologic and clinical differences of disease related to serology. MATERIALS AND METHODS: This was a prospective study, which included 98 IIM patients, divided into serological subgroups: anti-aminoacyl-transfer-RNA-synthetases (anti-ARS) positive and myositis-specific autoantibodies (MSA) negative.For each baseline CT the total semiquantitative score of Warrick (WS) and the automated software (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) quantitative scores interstitial lung disease % (ILD%) and vascular-related structure % (VRS%) were calculated. Pulmonary function tests included total lung capacity % (TLC%), forced vital capacity % (FVC%), and diffusing capacity of the lung for carbon monoxide % (DLCO%). RESULTS: Inverse correlations ( P <0.001) between the radiologic scores and the functional scores DLCO% and TLC% were found, the most relevant being between ILD% and DLCO% (ρ=-0.590), VRS% and DLCO% (ρ=-0.549), and WS and DLCO% (ρ=-0.471).Positive correlations between ILD% and VRS% (ρ=0.916; P <0.001), WS and ILD% (ρ=0.663; ρ<0.001), and WS and VRS% (ρ=0.637; P <0.001) were obtained.Statistically significant higher values of WS, ILD%, and VRS% were found in the anti-ARS group (WS=15; ILD%=11; VRS%=3.5) compared with the MSA negative one (WS=2.5; ILD%=0.84; VRS%=2.2).The nonspecific interstitial pneumonia pattern was dominant. No statistically significant differences emerged at pulmonary function tests. CONCLUSIONS: In this study, ILD in anti-ARS-positive and MSA-negative groups was defined through semiquantitative and quantitative analysis of lung CT. The inverse correlations between the radiologic scores and TLC% and DLCO% ( P <0.001) confirm the role of lung CT in the evaluation of ILD in IIM.