The use of chest magnetic resonance imaging in interstitial lung disease: a systematic review

Thin-slices multi-detector computed tomography (MDCT) plays a key role in the differential diagnosis of interstitial lung disease (ILD). However, thin-slices MDCT has a limited ability to detect active inflammation, which is an important target of newly developed ILD drug therapy. Magnetic resonance imaging (MRI), thanks to its multi-parameter capability, provides better tissue characterisation than thin-slices MDCT.Our aim was to summarise the current status of MRI applications in ILD and to propose an ILD-MRI protocol. A systematic literature search was conducted for relevant studies on chest MRI in patients with ILD.We retrieved 1246 papers of which 55 original papers were selected for the review. We identified 24 studies comparing image quality of thin-slices MDCT and MRI using several MRI sequences. These studies described new MRI sequences to assess ILD parenchymal abnormalities, such as honeycombing, reticulation and ground-glass opacity. Thin-slices MDCT remains superior to MRI for morphological imaging. However, recent studies with ultra-short echo-time MRI showed image quality comparable to thin-slices MDCT. Several studies demonstrated the added value of chest MRI by using functional imaging, especially to detect and quantify inflammatory changes.We concluded that chest MRI could play a role in ILD patients to differentiate inflammatory and fibrotic changes and to assess efficacy of new ILD drugs

Role of quantitative imaging and deep learning in interstitial lung diseases

Interstitial lung disease (ILD) are a large group of diffuse lung diseases characterized by similar clinical, pathological and radiological features. High resolution computed tomography (HRCT) has a central role in ILD diagnosis and management. In the last few years, computer-aided methods as Quantitative Computer Tomography (QCT) and Artificial Intelligence (AI) software were proposed as a source of reliable quantitative imaging biomarkers. The present review aimed to summarize and describe the current QCT and AI methods and to evaluate their potential diagnostic and prognostic role. The first attempt to a quantitative analysis of HRCT in ILD is represented by the density histogram analysis with the definition of two new parameter, Kurtosis and Skewness. Then texture analysis tools were developed as Adaptive Multiple Features Method (AMFM), Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER), Quantitative Lung Fibrosis (QLF) and Automated Quantification System (AQS). The introduction of AI technologies further increased the amount of objective and reproducible biomarkers. The diagnostic and prognostic role of QCT and AI methods was analyzed and confirmed in various studies, as reported in the review. QCT and AI technologies application led to the introduction of new objective biomarkers with relevant diagnostic and prognostic implications. However, there is still the need for more prospective study and the creation of open-source datasets would help to assess QCT and AI methods efficacy and to compare them

Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis

Introduction: Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS). Materials and methods: We performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care. Results: There was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001). Conclusion: Our study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality. Key points: • The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients. • VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001. • Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality

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

LOW PREVALENCE OF THE SOMATIC M918T RET MUTATION IN MICRO-MEDULLARY THYROID CANCER.

Background: The prevalence of RET somatic mutations in sporadic medullary thyroid cancer (MTCs) is approximately 40-50%, and the most frequent somatic mutation is M918T. RET-positive MTCs have been demonstrated to have a more advanced stage at diagnosis and a worse outcome. Aims: The aim of the present work was to compare the prevalence of RET somatic mutations in sporadic microMTCs ( 1 and 2 and 3 cm. Results: The overall prevalence of the somatic M918T RET mutation was 19.4% (31/160). RET mutations were distributed differently among the four groups. The prevalence was 11.3% (6/53) in group A, 11.8% (8/60) in group B, 31.8% (7/22) in group C and 58.8% (10/17) in group D, exhibiting an increase with increasing size of the tumor. When comparing the prevalence of mutations in the four groups, we found a lower prevalence in microMTCs (p< 0.0001). Conclusions: The overall prevalence of RET somatic mutations was lower than expected, and the prevalence of the somatic M918T RET mutation was significantly lower in microMTCs than in larger tumors. To explain this finding we can hypothesize either that other oncogene(s) might be responsible for the majority of microMTC, thus identifying a tumor subset, or that RET mutation might, or not, occur later during the tumor progression

Low diaphragm muscle mass predicts adverse outcome in patients hospitalized for COVID-19 pneumonia: An exploratory pilot study

BACKGROUND: The aim of this study was to evaluate whether measurement of diaphragm thickness (DT) by ultrasonography may be a clinically useful noninvasive method for identifying patients at risk of adverse outcomes defined as need of invasive mechanical ventilation or death. METHODS: We prospectively enrolled 77 patients with laboratory-confirmed COVID-19 infection admitted to our intermediate care unit in Pisa between March 5 and March 30, 2020, with follow-up until hospital discharge or death. Logistic regression was used identify variables potentially associated with adverse outcomes and those P&lt;0.10 were entered into a multivariate logistic regression model. cumulative probability for lack of adverse outcomes in patients with or without low baseline diaphragm muscle mass was calculated with the Kaplan-Meier product-limit estimator. RESULTS: The main findings of this study are that: 1) patients who developed adverse outcomes had thinner diaphragm than those who did not (2.0 vs. 2.2 mm, P=0.001); and 2) DT and lymphocyte count were independent significant predictors of adverse outcomes, with end-expiratory DT being the strongest (8=-708; oR=0.492; P=0.018). coNcLUSioNS: Diaphragmatic ultrasound may be a valid tool to evaluate the risk of respiratory failure. Evaluating the need of mechanical ventilation treatment should be based not only on Pao2/Fio2, but on a more comprehensive assess¬ ment including DT because if the lungs become less compliant a thinner diaphragm, albeit free of intrinsic abnormality, may become exhausted, thus contributing to severe respiratory failure

Mortality surrogates in combined pulmonary fibrosis and emphysema

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) with co-existent 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=317:183; replication cohort=358:152), who were subgrouped using 10%, or 15% visual emphysema thresholds, and an unsupervised machine learning model considering emphysema and ILD extents. Baseline characteristics, 1-year relative FVC and diffusing capacity of the lung for carbon monoxide (DLco) 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 DLco compared to CPFE patients with <10% emphysema. Using multivariable Cox regression analyses in patients with ≥10% emphysema, 1-year DLco 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, DLco decline should be considered in patients with ≥10% emphysema and a ≥5% 1-year relative FVC decline threshold considered in non-CPFE IPF patients

Patterns of Long COVID Symptoms: A Multi-Center Cross Sectional Study

Background: Long COVID has become a burden on healthcare systems worldwide. Research into the etiology and risk factors has been impeded by observing all diverse manifestations as part of a single entity. We aimed to determine patterns of symptoms in convalescing COVID-19 patients. Methods: Symptomatic patients were recruited from four countries. Data were collected regarding demographics, comorbidities, acute disease and persistent symptoms. Factor analysis was performed to elucidate symptom patterns. Associations of the patterns with patients’ characteristics, features of acute disease and effect on daily life were sought. Results: We included 1027 symptomatic post-COVID individuals in the analysis. The majority of participants were graded as having a non-severe acute COVID-19 (N = 763, 74.3%). We identified six patterns of symptoms: cognitive, pain-syndrome, pulmonary, cardiac, anosmia-dysgeusia and headache. The cognitive pattern was the major symptoms pattern, explaining 26.2% of the variance; the other patterns each explained 6.5–9.5% of the variance. The cognitive pattern was higher in patients who were outpatients during the acute disease. The pain-syndrome pattern was associated with acute disease severity, higher in women and increased with age. The pulmonary pattern was associated with prior lung disease and severe acute disease. Only two of the patterns (cognitive and cardiac) were associated with failure to return to pre-COVID occupational and physical activity status. Conclusion: Long COVID diverse symptoms can be grouped into six unique patterns. Using these patterns in future research may improve our understanding of pathophysiology and risk factors of persistent COVID, provide homogenous terminology for clinical research, and direct therapeutic interventions