CT-Guided Core-Needle Biopsy of Pulmonary Lesions Associated With Cystic Airspaces: A Case-Control Study.

Background: Concern may exist that pulmonary lesions associated with cystic airspaces are at risk of increased biopsy complications or lower biopsy accuracy given challenges in targeting tissue abutting or intermingled with the cystic airspaces. Objective: To evaluate the safety and diagnostic performance of CT-guided core-needle biopsy (CNB) of pulmonary lesions with cystic airspaces. Methods: This retrospective study included 90 patients (median age, 69.5 years; 28 female, 62 male) who underwent CT-guided CNB of pulmonary lesions associated with cystic airspaces (based on review of procedural images) from February 2010 to December 2022 and a matched control group (2:1 ratio) of 180 patients (median age, 68.0 years; 56 female, 124 male) who underwent CNB of noncystic noncavitary lesions during the same period. The groups were compared in terms of complications, nondiagnostic biopsies (i.e., nonspecific benignities, atypical cells, or insufficient specimens), and CNB diagnostic performance for detecting malignancy using as reference the final diagnosis from a joint review of all available records. For lesions associated with cystic airspaces that underwent surgical resection after CNB, histologic slides were re-reviewed to assess cystic airspace etiology. Results: The final diagnosis was malignant in 90% (81/90) of lesions associated with cystic airspaces and 92% (165/180) of noncystic noncavitary lesions. Patients with lesions associated with cystic airspaces and patients with noncystic noncavitary lesions showed no significant difference in frequencies of complication (all: 40% [36/90] vs 38% [68/180], p=.79; major: 4% [4/90] vs 6% [10/180], p=.78; minor: 36% [32/90] vs 32% [58/180], p=.59), frequency of nondiagnostic biopsies (12% [11/90] vs 9% [16/180], p=.40), or diagnostic performance (accuracy: 94.% [85/90] vs 97% [175/180], p=.50; sensitivity: 94% [76/81] vs 97% [160/165], p=.50; specificity: 100% [9/9] vs 100% [15/15]; p>.99), respectively. All false-negative results for malignancy in both groups occurred in patients with nondiagnostic CNB results. Among lesions associated with cystic airspaces that were resected after CNB (all malignant), the cystic airspaces most commonly represented tumor degeneration (22/31, 71%). Conclusion: CT-guided CNB is safe and accurate for assessing pulmonary lesions associated with cystic airspaces. Clinical Impact: CNB may help avoid a missed or delayed cancer diagnosis in pulmonary lesions with cystic airspaces.

Cardiovascular Disease from Pathophysiology to Risk Estimation: Is Inflammation Estimated through Perivascular Attenuation on Computed Tomography the Key?

(1) Background: Systemic inflammation stands as a well-established risk factor for ischemic cardiovascular disease, as well as a contributing factor in the development of cardiac arrhythmias, notably atrial fibrillation. Furthermore, scientific studies have brought to light the pivotal role of localized vascular inflammation in the initiation, progression, and destabilization of coronary atherosclerotic disease. (2) Methods: We comprehensively review recent, yet robust, scientific evidence elucidating the use of perivascular adipose tissue attenuation measurement on computed tomography applied to key anatomical sites. Specifically, the investigation extends to the internal carotid artery, aorta, left atrium, and coronary arteries. (3) Conclusions: The examination of perivascular adipose tissue attenuation emerges as a non-invasive and indirect means of estimating localized perivascular inflammation. This measure is quantified in Hounsfield units, indicative of the inflammatory response elicited by dense adipose tissue near the vessel or the atrium. Particularly noteworthy is its potential utility in assessing inflammatory processes within the coronary arteries, evaluating coronary microvascular dysfunction, appraising conditions within the aorta and carotid arteries, and discerning inflammatory states within the atria, especially in patients with atrial fibrillation. The widespread applicability of perivascular adipose tissue attenuation measurement underscores its significance as a diagnostic tool with considerable potential for enhancing our understanding and management of cardiovascular diseases.

Multiphase micro-computed tomography reconstructions provide dynamic respiratory function in a mouse lung fibrosis model.

Micro-computed tomography derived functional biomarkers used in lung disease research can significantly complement end-stage histomorphometric measures while also allowing for longitudinal studies. However, no approach for visualizing lung dynamics across a full respiratory cycle has yet been described. Using bleomycin-induced lung fibrosis and the antifibrotic drug nintedanib as a test model, we implemented a four-dimensional (4D) micro-CT imaging approach consisting of 30 reconstructed volumes per respiratory cycle, coupled with deep-learning-assisted segmentation of lung volumes. 4D micro-CT provided an accurate description of inhalatory and exhalatory lung dynamics under resting conditions and revealed an inflammation-related obstructive pattern at day 7, followed by a restrictive pattern associated with fibrosis development at day 21. A milder restriction and fibrotic pathology resulted from nintedanib treatment. The similarity of 4D micro-CT data with those produced by diagnostic measurements, also points to its great potential as an exploratory tool for the discovery of clinically relevant therapeutic compounds.

Study rationale and design of the PEOPLHE trial.

PURPOSE: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS: PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION: The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.

Coronary inflammation on chest computed tomography and COVID-19 mortality.

OBJECTIVES: The main factors associated with coronavirus disease-19 (COVID-19) mortality are age, comorbidities, pattern of inflammatory response, and SARS-CoV-2 lineage involved in infection. However, the clinical course of the disease is extremely heterogeneous, and reliable biomarkers predicting adverse prognosis are lacking. Our aim was to elucidate the prognostic role of a novel marker of coronary artery disease inflammation, peri-coronary adipose tissue attenuation (PCAT), available from high-resolution chest computed tomography (HRCT) in COVID-19 patients with severe disease requiring hospitalization. METHODS: Two distinct groups of patients were admitted to Parma University Hospital in Italy with COVID-19 in March 2020 and March 2021 (first- and third-wave peaks of the COVID-19 pandemic in Italy, with the prevalence of wild-type and B.1.1.7 SARS-CoV-2 lineage, respectively) were retrospectively enrolled. The primary endpoint was in-hospital mortality. Demographic, clinical, laboratory, HRCT data, and coronary artery HRCT features (coronary calcium score and PCAT attenuation) were collected to show which variables were associated with mortality. RESULTS: Among the 769 patients enrolled, 555 (72%) were discharged alive, and 214 (28%) died. In multivariable logistic regression analysis age (p < 0.001), number of chronic illnesses (p < 0.001), smoking habit (p = 0.006), P/F ratio (p = 0.001), platelet count (p = 0.002), blood creatinine (p < 0.001), non-invasive mechanical ventilation (p < 0.001), HRCT visual score (p < 0.001), and PCAT (p < 0.001), but not the calcium score, were independently associated with in-hospital mortality. CONCLUSION: Coronary inflammation, measured with PCAT on non-triggered HRCT, appeared to be independently associated with higher mortality in patients with severe COVID-19, while the pre-existent coronary atherosclerotic burden was not associated with adverse outcomes after adjustment for covariates. CLINICAL RELEVANCE STATEMENT: The current study demonstrates that a relatively simple measurement, peri-coronary adipose tissue attenuation (PCAT), available ex-post from standard high-resolution computed tomography, is strongly and independently associated with in-hospital mortality. KEY POINTS: • Coronary inflammation can be measured by the attenuation of peri-coronary adipose tissue (PCAT) on high-resolution CT (HRCT) without contrast media. • PCAT is strongly and independently associated with in-hospital mortality in SARS-CoV-2 patients. • PCAT might be considered an independent prognostic marker in COVID-19 patients if confirmed in other studies.

A formula for predicting emphysema extent in combined idiopathic pulmonary fibrosis and emphysema.

BACKGROUND: No single pulmonary function test captures the functional effect of emphysema in idiopathic pulmonary fibrosis (IPF). Without experienced radiologists, other methods are needed to determine emphysema extent. Here, we report the development and validation of a formula to predict emphysema extent in patients with IPF and emphysema. METHODS: The development cohort included 76 patients with combined IPF and emphysema at the Royal Brompton Hospital, London, United Kingdom. The formula was derived using stepwise regression to generate the weighted combination of pulmonary function data that fitted best with emphysema extent on high-resolution computed tomography. Test cohorts included patients from two clinical trials (n = 455 [n = 174 with emphysema]; NCT00047645, NCT00075998) and a real-world cohort from the Royal Brompton Hospital (n = 191 [n = 110 with emphysema]). The formula is only applicable for patients with IPF and concomitant emphysema and accordingly was not used to detect the presence or absence of emphysema. RESULTS: The formula was: predicted emphysema extent = 12.67 + (0.92 x percent predicted forced vital capacity) - (0.65 x percent predicted forced expiratory volume in 1 second) - (0.52 x percent predicted carbon monoxide diffusing capacity). A significant relationship between the formula and observed emphysema extent was found in both cohorts (R2 = 0.25, P < 0.0001; R2 = 0.47, P < 0.0001, respectively). In both, the formula better predicted observed emphysema extent versus individual pulmonary function tests. A 15% emphysema extent threshold, calculated using the formula, identified a significant difference in absolute changes from baseline in forced vital capacity at Week 48 in patients with baseline-predicted emphysema extent < 15% versus ≥ 15% (P = 0.0105). CONCLUSION: The formula, designed for use in patients with IPF and emphysema, demonstrated enhanced ability to predict emphysema extent versus individual pulmonary function tests. TRIAL REGISTRATION: NCT00047645; NCT00075998.

High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study.

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis.

The impact of muscle mass and myosteatosis on mortality in critically ill patients with Sars-Cov2-related pneumonia.

BACKGROUND & AIMS: Sars-Cov-2 pneumonia can lead to severe complications, requiring invasive mechanical ventilation and admission to the intensive care unit (ICU). Low muscle quantity and quality (the latter evaluated by the amount of ectopic fat infiltration in the muscle [myosteatosis]) at ICU admission are associated with worse outcomes in critically ill patients. The purpose of the present study is to assess muscle mass and myosteatosis of paravertebral skeletal muscle, in critically ill patients with Sars-Cov2 pneumonia and its association with mortality. METHODS: We conducted a retrospective observational study in 110 critically ill patients with severe Sars-Cov-2 pneumonia that had a high - resolution chest Computerized Tomography (HR-CT) at ICU admission. We acquired CT images at the level of the thoracic 12 (T12) vertebral body and measured skeletal muscle area (SMA), intermuscular adipose tissue (IMAT), and low attenuation muscle area (LAMA). Patients were followed until ICU mortality or discharge. RESULTS: Patients were 59.8 ± 8.1 years old, 77% were male. Seventy-nine percent of patients were considered at nutritional risk, and 22% were obese. Average Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 17 ± 5.4, and the overall ICU mortality was 48,2% (53/110). At ICU admission, both parameters of myosteatosis were associated with higher mortality (IMAT [per 10% increase] HR: 2.01 (95% Confidence Interval [CI] 1.27 to 3.17), P = 0.003; LAMA HR [per 10% increase]: 1.53 (95% CI 1.10 to 2.13), P = 0.012). CONCLUSION: Myosteatosis as assessed by CT scans plays a relevant role as a prognostic marker in critically ill patients with Sars-Cov2 severe pneumonia.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.

BACKGROUND: The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. METHODS: The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). RESULTS: The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). CONCLUSIONS: At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Walking the path of treatable traits in interstitial lung diseases.

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.

Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.

Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; P = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; P = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; P < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; P < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT02837809 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Horst and Nishino in this issue.

A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans.

OBJECTIVES: The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. MATERIALS AND METHODS: The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. RESULTS: AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. CONCLUSIONS: A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology".

Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.

Structural changes in lung morphology detected by MRI after modulating therapy with elexacaftor/tezacaftor/ivacaftor in adolescent and adult patients with cystic fibrosis.

INTRODUCTION: Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) improves CFTR function in cystic fibrosis (CF) patients homozygous or heterozygous for F508del mutation. The aim of the study was to evaluate the response to ELX/TEZ/IVA treatment both clinically and morphologically in terms of bronchiectasis, bronchial wall thickening, mucus plugging, abscess and consolidations. METHODS: We retrospectively collected data from CF patients followed at Parma CF Centre (Italy) treated by ELX/TEZ/IVA between March and November 2021. Post-treatment changes in respiratory function, quality of life, sweat chloride concentration, body mass index, pulmonary exacerbations and lung structure by chest magnetic resonance imaging (MRI) were assessed. T2-and T1-weighted sequences were acquired with a 20 min-long scanning protocol on a 1.5T MRI scanner (Philips Ingenia) without administration of intravenous contrast media. RESULTS: 19 patients (32.5 ± 10.2 years) were included in the study. After 6 months of treatment with ELX/TEZ/IVA, MRI showed significant improvements in the morphological score (p < 0.001), with a reduction in bronchial wall thickening (p < 0.001) and mucus plugging (p 0.01). Respiratory function showed significant improvement in predicted FEV1% (58.5 ± 17.5 vs 71.4 ± 20.1, p < 0.001), FVC% (79.0 ± 11.1 vs 88.3 ± 14.4, p < 0.001), FEV1/FVC (0.61 ± 0.16 vs 0.67 ± 0.15, <0.001) and LCI2.5% (17.8 ± 4.3 vs 15.8 ± 4.1 p < 0.005). Significant improvement was found in body mass index (20.6 ± 2.7 vs 21.9 ± 2.4, p < 0.001), pulmonary exacerbations (2.3 ± 1.3 vs 1.4 ± 1.3 p 0.018) and sweat chloride concentration (96.5 ± 36.6 vs 41.1 ± 16.9, p < 0.001). CONCLUSIONS: Our study confirms the efficacy of ELX/TEZ/IVA in CF patients not only from a clinical point of view but also in terms of morphological changes of the lungs.

A fully automated micro­CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory µCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.

Cardiac Magnetic Resonance for Prophylactic Implantable-Cardioverter Defibrillator Therapy in Ischemic Cardiomyopathy: The DERIVATE-ICM International Registry.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) therapy is the most effective prophylactic strategy against sudden cardiac death (SCD) in patients with ischemic cardiomyopathy (ICM) and left ventricle ejection fraction (LVEF) ≤35% as detected by transthoracic echocardiograpgy (TTE). This approach has been recently questioned because of the low rate of ICD interventions in patients who received implantation and the not-negligible percentage of patients who experienced SCD despite not fulfilling criteria for implantation. OBJECTIVES: The DERIVATE-ICM registry (CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy; NCT03352648) is an international, multicenter, and multivendor study to assess the net reclassification improvement (NRI) for the indication of ICD implantation by the use of cardiac magnetic resonance (CMR) as compared to TTE in patients with ICM. METHODS: A total of 861 patients with ICM (mean age 65 ± 11 years, 86% male) with chronic heart failure and TTE-LVEF <50% participated. Major adverse arrhythmic cardiac events (MAACE) were the primary endpoints. RESULTS: During a median follow-up of 1,054 days, MAACE occurred in 88 (10.2%). Left ventricular end-diastolic volume index (HR: 1.007 [95% CI: 1.000-1.011]; P = 0.05), CMR-LVEF (HR: 0.972 [95% CI: 0.945-0.999]; P = 0.045) and late gadolinium enhancement (LGE) mass (HR: 1.010 [95% CI: 1.002-1.018]; P = 0.015) were independent predictors of MAACE. A multiparametric CMR weighted predictive derived score identifies subjects at high risk for MAACE compared with TTE-LVEF cutoff of 35% with a NRI of 31.7% (P = 0.007). CONCLUSIONS: The DERIVATE-ICM registry is a large multicenter registry showing the additional value of CMR to stratify the risk for MAACE in a large cohort of patients with ICM compared with standard of care.

Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial.

Coronary artery calcium (CAC) is a known risk factor for cardiovascular (CV) events and mortality but is not yet routinely evaluated in low-dose computed tomography (LDCT)-based lung cancer screening (LCS). The present analysis explored the capacity of a fully automated CAC scoring to predict 12-year mortality in the Multicentric Italian Lung Detection (MILD) LCS trial. The study included 2239 volunteers of the MILD trial who underwent a baseline LDCT from September 2005 to January 2011, with a median follow-up of 190 months. The CAC score was measured by a commercially available fully automated artificial intelligence (AI) software and stratified into five strata: 0, 1-10, 11-100, 101-400, and > 400. Twelve-year all-cause mortality was 8.5% (191/2239) overall, 3.2% with CAC = 0, 4.9% with CAC = 1-10, 8.0% with CAC = 11-100, 11.5% with CAC = 101-400, and 17% with CAC > 400. In Cox proportional hazards regression analysis, CAC > 400 was associated with a higher 12-year all-cause mortality both in a univariate model (hazard ratio, HR, 5.75 [95% confidence interval, CI, 2.08-15.92] compared to CAC = 0) and after adjustment for baseline confounders (HR, 3.80 [95%CI, 1.35-10.74] compared to CAC = 0). All-cause mortality significantly increased with increasing CAC (7% in CAC ≤ 400 vs. 17% in CAC > 400, Log-Rank p-value <0.001). Non-cancer at 12 years mortality was 3% (67/2239) overall, 0.8% with CAC = 0, 1.0% with CAC = 1-10, 2.9% with CAC = 11-100, 3.6% with CAC = 101-400, and 8.2% with CAC > 400 (Grey's test p < 0.001). In Fine and Gray's competing risk model, CAC > 400 predicted 12-year non-cancer mortality in a univariate model (sub-distribution hazard ratio, SHR, 10.62 [95% confidence interval, CI, 1.43-78.98] compared to CAC = 0), but the association was no longer significant after adjustment for baseline confounders. In conclusion, fully automated CAC scoring was effective in predicting all-cause mortality at 12 years in a LCS setting.

Lung dual energy CT: Impact of different technological solutions on quantitative analysis.

PURPOSE: To evaluated the accuracy of spectral parameters quantification of four different CT scanners in dual energy examinations of the lung using a dedicated phantom. METHOD: Measurements were made with different technologies of the same vendor: one dual source CT scanner (DSCT), one TwinBeam (i.e. split filter) and two sequential acquisition single source scanners (SSCT). Angular separation of Calcium and Iodine signals were calculated from scatter plots of low-kVp versus high-kVp HUs. Electron density (ρe), effective atomic number (Zeff) and Iodine concentration (Iconc) were measured using Syngo.via software. Accuracy (A) of ρe, Zeff and Iconc was evaluated as the absolute percentage difference (D%) between reference values and measured ones, while precision (P) was evaluated as the variability σ obtained by repeating the measurement with different acquisition/reconstruction settings. RESULTS: Angular separation was significantly larger for DSCT (α = 9.7°) and for sequential SSCT (α = 9.9°) systems. TwinBeam was less performing in material separation (α = 5.0°). The lowest average A was observed for TwinBeam (Aρe = [4.7 ± 1.0], AZ = [9.1 ± 3.1], AIconc = [19.4 ± 4.4]), while the best average A was obtained for Flash (Aρe = [1.8 ± 0.4], AZ = [3.5 ± 0.7], AIconc = [7.3 ± 1.8]). TwinBeam presented inferior average P (Pρe = [0.6 ± 0.1], PZ = [1.1 ± 0.2], PIconc = [10.9 ± 4.9]), while other technologies demonstrate a comparable average. CONCLUSIONS: Different technologies performed material separation and spectral parameter quantification with different degrees of accuracy and precision. DSCT performed better while TwinBeam demonstrated not excellent performance. Iodine concentration measurements exhibited high variability due to low Iodine absolute content in lung nodules, thus limiting its clinical usefulness in pulmonary applications.