Common and Uncommon CT Findings in CVID-Related GL-ILD: Correlations with Clinical Parameters, Therapeutic Decisions and Potential Implications in the Differential Diagnosis.

PURPOSE: To investigate computed tomography (CT) findings of Granulomatous Lymphocytic Interstitial Lung Disease (GL-ILD) in Common Variable Immunodeficiency (CVID), also in comparison with non-GL-ILD abnormalities, correlating GL-ILD features with functional/immunological parameters and looking for GL-ILD therapy predictive elements. METHODS: CT features of 38 GL-ILD and 38 matched non-GL-ILD subjects were retrospectively described. Correlations of GL-ILD features with functional/immunological features were assessed. A logistic regression was performed to find a predictive model of GL-ILD therapeutic decisions. RESULTS: Most common GL-ILD CT findings were bronchiectasis, non-perilymphatic nodules, consolidations, Ground Glass Opacities (GGO), bands and enlarged lymphnodes. GL-ILD was usually predominant in lower fields. Multiple small nodules (≤10 mm), consolidations, reticulations and fibrotic ILD are more indicative of GL-ILD. Bronchiectasis, GGO, Reticulations and fibrotic ILD correlated with decreased lung performance. Bronchiectasis, GGO and fibrotic ILD were associated with low IgA levels, whereas high CD4+ T cells percentage was related to GGO. Twenty out of 38 patients underwent GL-ILD therapy. A model combining Marginal Zone (MZ) B cells percentage, IgA levels, lower field consolidations and lymphnodes enlargement showed a good discriminatory capacity with regards to GL-ILD treatment. CONCLUSIONS: GL-ILD is a lower field predominant disease, commonly characterized by bronchiectasis, non-perilymphatic small nodules, consolidations, GGO and bands. Multiple small nodules, consolidations, reticulations and fibrotic ILD may suggest the presence of GL-ILD in CVID. MZ B cells percentage, IgA levels at diagnosis, lower field consolidations and mediastinal lymphnodes enlargement may predict the need of a specific GL-ILD therapy.

Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation.

BACKGROUND: Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes. METHODS: We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints. RESULTS: 97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10-20% up to ~45-55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle. CONCLUSIONS: A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.

Performance of Node-RADS Scoring System for a Standardized Assessment of Regional Lymph Nodes in Bladder Cancer Patients.

BACKGROUND: Current cross-sectional imaging modalities exhibit heterogenous diagnostic performances for the detection of a lymph node invasion (LNI) in bladder cancer (BCa) patients. Recently, the Node-RADS score was introduced to provide a standardized comprehensive evaluation of LNI, based on a five-item Likert scale accounting for both size and configuration criteria. In the current study, we hypothesized that the Node-RADS score accurately predicts the LNI and tested its diagnostic performance. METHODS: We retrospectively reviewed BCa patients treated with radical cystectomy (RC) and bilateral extended pelvic lymph node dissection, from January 2019 to June 2022. Patients receiving preoperative systemic chemotherapy were excluded. A logistic regression analysis tested the correlation between the Node-RADS score and LNI both at patient and lymph-node level. The ROC curves and the AUC depicted the overall diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for different cut-off values (>1, >2, >3, >4). RESULTS: Overall, data from 49 patients were collected. Node-RADS assigned on CT scans images, was found to independently predict the LNI after an adjusted multivariable regression analysis, both at the patient (OR 3.36, 95%CI 1.68-9.40, p = 0.004) and lymph node (OR 5.18, 95%CI 3.39-8.64, p < 0.001) levels. Node-RADS exhibited an AUC of 0.87 and 0.91 at the patient and lymph node levels, respectively. With increasing Node-RADS cut-off values, the specificity and PPV increased from 57.1 to 97.1% and from 48.3 to 83.3%, respectively. Conversely, the sensitivity and NPV decreased from 100 to 35.7% and from 100 to 79.1%, respectively. Similar trends were recorded at the lymph node level. Potentially, Node-RADS > 2 could be considered as the best cut-off value due to balanced values at both the patient (77.1 and 78.6%, respectively) and lymph node levels (82.4 and 93.4%, respectively). CONCLUSIONS: The current study lays the foundation for the introduction of Node-RADS for the regional lymph-node evaluation in BCa patients. Interestingly, the Node-RADS score exhibited a moderate-to-high overall accuracy for the identification of LNI, with the possibility of setting different cut-off values according to specific clinical scenarios. However, these results need to be validated on larger cohorts before drawing definitive conclusions.

Impact of Hypogammaglobulinemia on the Course of COVID-19 in a Non-Intensive Care Setting: A Single-Center Retrospective Cohort Study.

Background: Severity and mortality of COVID-19 largely depends on the ability of the immune system to clear the virus. Among various comorbidities potentially impacting on this process, the weight and the consequences of an antibody deficiency have not yet been clarified. Methods: We used serum protein electrophoresis to screen for hypogammaglobulinemia in a cohort of consecutive adult patients with COVID-19 pneumonia, hospitalized in non-intensive care setting between December 2020 and January 2021. The disease severity, measured by a validated score and by the need for semi intensive (sICU) or intensive care unit (ICU) admission, and the 30-day mortality was compared between patients presenting hypogammaglobulinemia (HYPO) and without hypogammaglobulinemia (no-HYPO). Demographics, comorbidities, COVID-19 specific treatment during the hospital stay, disease duration, complications and laboratory parameters were also evaluated in both groups. Results: We enrolled 374 patients, of which 39 represented the HYPO cohort (10.4%). In 10/39 the condition was previously neglected, while in the other 29/39 hematologic malignancies were common (61.5%); 2/39 were on regular immunoglobulin replacement therapy (IgRT). Patients belonging to the HYPO group more frequently developed a severe COVID-19 and more often required sICU/ICU admission than no-HYPO patients. IgRT were administered in 8/39 during hospitalization; none of them died or needed sICU/ICU. Among HYPO cohort, we observed a significantly higher prevalence of neoplastic affections, of active oncologic treatment and bronchiectasis, together with higher prevalence of viral and bacterial superinfections, mechanical ventilation, convalescent plasma and SARS-CoV-2 monoclonal antibodies administration during hospital stay, and longer disease duration. Multivariate logistic regression analysis and Cox proportional hazard regression confirmed the impact of hypogammaglobulinemia on the COVID-19 severity and the probability of sICU/ICU admission. The analysis of the mortality rate in the whole cohort showed no significant difference between HYPO and no-HYPO. Conclusions: Hypogammaglobulinemia, regardless of its cause, in COVID-19 patients hospitalized in a non-intensive care setting was associated to a more severe disease course and more frequent admission to s-ICU/ICU, particularly in absence of IgRT. Our findings emphasize the add-value of routine serum protein electrophoresis evaluation in patients admitted with COVID-19 to support clinicians in patient care and to consider IgRT initiation during hospitalization.

Management of respiratory tract exacerbations in people with cystic fibrosis: Focus on imaging.

Respiratory tract exacerbations play a crucial role in progressive lung damage of people with cystic fibrosis, representing a major determinant in the loss of functional lung tissue, quality of life and patient survival. Detection and monitoring of respiratory tract exacerbations are challenging for clinicians, since under- and over-treatment convey several risks for the patient. Although various diagnostic and monitoring tools are available, their implementation is hampered by the current definition of respiratory tract exacerbation, which lacks objective "cut-offs" for clinical and lung function parameters. In particular, the latter shows a large variability, making the current 10% change in spirometry outcomes an unreliable threshold to detect exacerbation. Moreover, spirometry cannot be reliably performed in preschool children and new emerging tools, such as the forced oscillation technique, are still complementary and need more validation. Therefore, lung imaging is a key in providing respiratory tract exacerbation-related structural and functional information. However, imaging encompasses several diagnostic options, each with different advantages and limitations; for instance, conventional chest radiography, the most used radiological technique, may lack sensitivity and specificity in respiratory tract exacerbations diagnosis. Other methods, including computed tomography, positron emission tomography and magnetic resonance imaging, are limited by either radiation safety issues or the need for anesthesia in uncooperative patients. Finally, lung ultrasound has been proposed as a safe bedside option but it is highly operator-dependent and there is no strong evidence of its possible use during respiratory tract exacerbation. This review summarizes the clinical challenges of respiratory tract exacerbations in patients with cystic fibrosis with a special focus on imaging. Firstly, the definition of respiratory tract exacerbation is examined, while diagnostic and monitoring tools are briefly described to set the scene. This is followed by advantages and disadvantages of each imaging technique, concluding with a diagnostic imaging algorithm for disease monitoring during respiratory tract exacerbation in the cystic fibrosis patient.

Granulomatous Lymphocytic Interstitial Lung Disease (GLILD) in Common Variable Immunodeficiency (CVID): A Multicenter Retrospective Study of Patients From Italian PID Referral Centers.

Background: Granulomatous and Lymphocytic Interstitial Lung Diseases (GLILD) is a severe non-infectious complication of Common Variable Immunodeficiency (CVID), often associated with extrapulmonary involvement. Due to a poorly understood pathogenesis, GLILD diagnosis and management criteria still lack consensus. Accordingly, it is a relevant cause of long-term loss of respiratory function and is closely associated with a markedly reduced survival. The aim of this study was to describe clinical, immunological, laboratory and functional features of GLILD, whose combination in a predictive model might allow a timely diagnosis. Methods: In a multicenter retrospective cross-sectional study we enrolled 73 CVID patients with radiologic features of interstitial lung disease (ILD) associated to CVID (CVID-ILD) and 125 CVID patients without ILD (controls). Of the 73 CVID-ILD patients, 47 received a definite GLILD diagnosis while 26 received a clinical-radiologic diagnosis of CVID related ILD defined as uILD. Results: In GLILD group we found a higher prevalence of splenomegaly (84.8 vs. 39.2%), autoimmune cytopenia (59.6 vs. 6.4%) and bronchiectasis (72.3 vs. 28%), and lower IgA and IgG serum levels at CVID diagnosis. GLILD patients presented lower percentage of switched-memory B cells and marginal zone B cells, and a marked increase in the percentage of circulating CD21lo B cells (14.2 vs. 2.9%). GLILD patients also showed lower total lung capacity (TLC 87.5 vs. 5.0%) and gas transfer (DLCO 61.5 vs. 5.0%) percent of predicted. By univariate logistic regression analysis, we found IgG and IgA levels at CVID diagnosis, presence of splenomegaly and autoimmune cytopenia, CD21lo B cells percentage, TLC and DCLO percent of predicted to be associated to GLILD. The joint analysis of four variables (CD21lo B cells percentage, autoimmune cytopenia, splenomegaly and DLCO percent of predicted), together in a multiple logistic regression model, yielded an area under the ROC curve (AUC) of 0.98 (95% CI: 0.95-1.0). The AUC was only slightly modified when pooling together GLILD and uILD patients (0.92, 95% CI: 0.87-0.97). Conclusions: we propose the combination of two clinical parameters (splenomegaly and autoimmune cytopenia), one lung function index (DLCO%) and one immunologic variable (CD21lo%) as a promising tool for early identification of CVID patients with interstitial lung disease, limiting the use of aggressive diagnostic procedures.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging.

BACKGROUND AND PURPOSE: Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY). METHODS: Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient. RESULTS: The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively. CONCLUSIONS: Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Pleuroparenchymal fibroelastosis in rheumatic autoimmune diseases: a systematic literature review.

OBJECTIVES: Pleuroparenchymal fibroelastosis (PPFE) is characterized by predominantly upper lobe pleural and subjacent parenchymal fibrosis; PPFE features were described in patients with rheumatic autoimmune diseases (RAID). A systematic literature review was performed to investigate the prevalence, prognosis and potential association of PPFE with previous immunosuppression in RAID. METHODS: EMBASE, Web of Science and PubMed databases were questioned from inception to 1 September 2019. Articles published in English and addressing PPFE in patients with RAID were selected. RESULTS: Twenty out of 794 papers were selected with a total of 76 cases of RAID-PPFE patients (20 SSc, 9 RA, 6 IIM6 primary SS, 5 overlap syndromes, 3 ANCA-associated vasculitides, 2 granulomatosis with polyangiitis, 1 microscopic polyangiitis, 1 UCTD, 1 SLE, 1 GCA and 21 patients with non-specified RAID). Dyspnoea was the most frequently reported symptom (37/48 patients, 77%). Patients frequently presented with a restrictive pattern and decline in diffusing lung capacity for carbon monoxide. During the follow-up, 7/12 patients had progression at imaging, 22/39 presented a generic clinical worsening, 19/38 had a functional deterioration and 15/43 remained stable. CONCLUSION: The present systematic literature review confirms that PPFE features are present in RAID. Rheumatologists should be aware of this new radiological pattern that holds a bad prognosis.

Development of digital phantoms based on a finite element model to simulate low-attenuation areas in CT imaging for pulmonary emphysema quantification.

PURPOSE: To develop an innovative finite element (FE) model of lung parenchyma which simulates pulmonary emphysema on CT imaging. The model is aimed to generate a set of digital phantoms of low-attenuation areas (LAA) images with different grades of emphysema severity. METHODS: Four individual parameter configurations simulating different grades of emphysema severity were utilized to generate 40 FE models using ten randomizations for each setting. We compared two measures of emphysema severity (relative area (RA) and the exponent D of the cumulative distribution function of LAA clusters size) between the simulated LAA images and those computed directly on the models output (considered as reference). RESULTS: The LAA images obtained from our model output can simulate CT-LAA images in subjects with different grades of emphysema severity. Both RA and D computed on simulated LAA images were underestimated as compared to those calculated on the models output, suggesting that measurements in CT imaging may not be accurate in the assessment of real emphysema extent. CONCLUSIONS: Our model is able to mimic the cluster size distribution of LAA on CT imaging of subjects with pulmonary emphysema. The model could be useful to generate standard test images and to design physical phantoms of LAA images for the assessment of the accuracy of indexes for the radiologic quantitation of emphysema.

Development and analysis of a finite element model to simulate pulmonary emphysema in CT imaging.

In CT imaging, pulmonary emphysema appears as lung regions with Low-Attenuation Areas (LAA). In this study we propose a finite element (FE) model of lung parenchyma, based on a 2-D grid of beam elements, which simulates pulmonary emphysema related to smoking in CT imaging. Simulated LAA images were generated through space sampling of the model output. We employed two measurements of emphysema extent: Relative Area (RA) and the exponent D of the cumulative distribution function of LAA clusters size. The model has been used to compare RA and D computed on the simulated LAA images with those computed on the models output. Different mesh element sizes and various model parameters, simulating different physiological/pathological conditions, have been considered and analyzed. A proper mesh element size has been determined as the best trade-off between reliable results and reasonable computational cost. Both RA and D computed on simulated LAA images were underestimated with respect to those calculated on the models output. Such underestimations were larger for RA (≈ -44 ÷ -26%) as compared to those for D (≈ -16 ÷ -2%). Our FE model could be useful to generate standard test images and to design realistic physical phantoms of LAA images for the assessment of the accuracy of descriptors for quantifying emphysema in CT imaging.