Atherosclerotic Coronary Plaque Features in Patients With Chronic Obstructive Pulmonary Disease and Acute Coronary Syndrome.

Previous studies reported a robust relation between chronic obstructive pulmonary disease (COPD) and coronary artery disease (CAD). Systemic inflammation has been proposed as possible pathogenetic mechanism linking these 2 entities, although data on atherosclerotic coronary features in COPD patients are lacking. We studied atherosclerotic coronary plaque features in COPD patients presenting with acute coronary syndrome (ACS) using optical coherence tomography (OCT). ACS patients who underwent intracoronary OCT imaging of the culprit vessel were enrolled. Coronary plaque characteristics and OCT-defined macrophage infiltration (MØI) were assessed by OCT. ACS patients were divided into 2 groups according to the presence of an established diagnosis of COPD, and plaque features at the culprit site and along the culprit vessel were compared between the groups. Of 146 ACS patients (mean age:66.1 ± 12.7 years, 109 men), 47 (32.2%) had COPD. Patients with COPD had significantly higher prevalence of MØI (78.7% vs 54.5%, p = 0.005) and thin cap fibroatheroma (TCFA) (48.9% vs 22.2%, p = 0.001) at the culprit site. In the multivariate logistic regression, COPD was independently associated with MØI (odds ratio [OR] 21.209, 95% confidence interval [CI] 1.679 to 267.910, p = 0.018) and TCFA at the culprit site (OR 5.345, 95% CI 1.386 to 20.616, p = 0.015). Similarly, COPD was independently associated with both MØI (OR 3.570, 95% CI 1.472 to 8.658, p = 0.005) and TCFA (OR 4.088, 95% CI 1.584 to 10.554, p = 0.004) along the culprit vessel. In conclusion, in ACS patients who underwent OCT imaging of the culprit vessel, COPD was an independent predictor of plaque inflammation and vulnerability. These results may suggest that a higher inflammatory milieu in COPD patients might enhance local coronary inflammation, promoting CAD development and plaque vulnerability.

Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs' Parenchymal Involvement Quantification in COVID-19 Patients.

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland-Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1-R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1-R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.

Controlling Nutritional Status Score as a Predictor for Chronic Obstructive Pulmonary Disease Exacerbation Risk in Elderly Patients.

The Controlling Nutritional Status (CONUT) score is a simple screening tool able to assess poor nutritional status as well as to predict clinical adverse outcomes in different clinical settings. No data are available in older patients with chronic obstructive pulmonary disease (COPD). This study aimed to investigate the CONUT score as a predictor of frequent exacerbations. We retrospectively enrolled 222 patients aged 65 years or older, classified in two groups according to the number of exacerbations (or hospitalizations because AECOPD) during the previous year. The two groups were further divided according to low (<5) or high (≥5) CONUT scores. A total of 67.2% of frequent exacerbators had a high CONUT score. These patients exhibited a significantly higher CAT score, lower FEV1 percentage value, and higher prevalence of severe GOLD stages compared to those with low CONUT. Multivariate analysis showed that a CONUT score ≥ 5 was the best independent predictor (OR 20.740, p < 0.001) of the occurrence of ≥2 exacerbations (or 1 hospitalization) during the previous year. The CONUT score seemed to have a high prognostic value for frequent exacerbations for COPD in older patients. The predictive role of different CONUT score cut-off values needs to be validated in larger COPD populations in future multi-center, prospective clinical studies.

Large strain correction for tunnel analyses considering hydromechanical coupling and ground anisotropy.

The deformations resulting from tunnel analyses for heavily squeezing ground may be very large, necessitating numerical formulations that consider geometric nonlinearity. Alternatively, for a certain class of problems, routine small strain analyses can be performed, and their results can be corrected to account for large strains by means of a simple hyperbolic expression proposed a few years ago. The present paper shows that this correction equation is sufficiently accurate for practical purposes even in the case of anisotropic material behaviour and for hydromechanically coupled, steady state or transient analyses of tunnels. The accuracy of the equation prediction varies amongst these cases but is satisfactory overall for the purpose of preliminary calculations, thus broadening its value and usefulness as a preliminary design tool.

Radiofrequency Ablation, Cryoablation, and Microwave Ablation for the Treatment of Small Renal Masses: Efficacy and Complications.

Over the last two decades the detection rate of small renal masses has increased, due to improving diagnostic accuracy, and nephron-sparing treatments have become the first-choice curative option for small renal masses. As a minimally invasive alternative, thermal ablation has increased in popularity, offering a good clinical outcome and low recurrence rate. Radiofrequency ablation, Cryoablation, and Microwave ablation are the main ablative techniques. All of them are mostly overlapping in term of cancer specific free survival and outcomes. These techniques require imaging study to assess lesions features and to plan the procedure: US, CT, and both of them together are the leading guidance alternatives. Imaging findings guide the interventional radiologist in assessing the risk of complication and possible residual disease after procedure. The purpose of this review is to compare different ablative modalities and different imaging guides, underlining the effectiveness, outcomes, and complications related to each of them, in order to assist the interventional radiologist in choosing the best option for the patient.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.