Quantitative and semi-quantitative computed tomography analysis of interstitial lung disease associated with systemic sclerosis: A longitudinal evaluation of pulmonary parenchyma and vessels

Objectives To evaluate interstitial lung disease associated with systemic sclerosis (SSc-ILD) and its changes during treatment by using quantitative analysis (QA) compared to semi-quantitative analysis (semiQA) of chest computed tomography (CT) scans. To assess the prognostic value of QA in predicting functional changes. Materials and methods We retrospectively selected 35 consecutive patients with SSc-ILD with complete pulmonary functional evaluation, Doppler-echocardiography, immunological tests, and chest CT scan at both baseline and follow-up after immunosuppressive therapy. CT images were analyzed by two chest radiologists for semiQA and by a computational platform for texture analysis of ILD patterns (CALIPER) for QA. Concordance between semiQA and QA was tested. Traction bronchiectasis severity was scored. Analysis of ROC curves was performed. Results Seventy CT scans were analyzed and QA failed in 4/70 scans. Thus, the final population included 31/35 patients (51.3\ub112.1 years). QA had a weak-to-good concordance with semiQA (ICC reticular:0.275; ICC ground-glass:0.667) and QA correlated better than semiQA (r = -0.3 to -0.74 vs r = -0.3 to -0.4) with functional parameters. Both methods correlated with traction bronchiectases score and pulmonary artery diameter at CT. A pulmonary artery diameter 29mm distinguished patients with lower lung volumes and ILD extent greater than 39% (p<0.001). Changes in QA patterns during treatment were not accurate (AUC: 0.50 to 0.70; p>0.05) in predicting disease progression as assessed by functional parameters, whereas variation in total lung volume at QA accurately predicted changes in the composite functional respiratory endpoint with FVC% and DLco% (AUC = 0.74; 95%CI: 0.54 to 0.93; p = 0.03). Conclusions Pulmonary QA of CT images can objectively quantify specific patterns of ILD changes during treatment in patients with SSc-ILD. Changes in QA patterns do not correlate with functional changes, but variation in total lung volume at QA accurately predicted changes in the composite functional respiratory endpoint with FVC% and DLco%. Pulmonary artery diameter at CT reflects the interstitial involvement, identifying patients with more severe prognosis

The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave

peer reviewedDuring the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

peer reviewedPurpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza

Radiation dose in non-dental cone beam CT applications: a systematic review

Background: Radiation-induced health risks are broadly questioned in the literature. As cone beam computed tomography (CBCT) is increasingly used in non-dental examinations, its effective dose needs to be known. This study aimed to review the published evidence on effective dose of non-dental CBCT for diagnostic use by focusing on dosimetry system used to estimate dose. Materials and methods: A systematic review of the literature was performed on 12 November 2017. All the literature up to this date was included. The PubMed and web of science databases were searched. Studies were screened for inclusion based on defined inclusion and exclusion criteria according to the preferred reporting items for systematic reviews. Results: Fifteen studies met the inclusion criteria and were included in our review. Thirteen and two of them examined one and two anatomical areas, respectively. The anatomical areas were: ear (6), paranasal sinuses (4), ankle (3), wrist (2), knee (1), and cervical spine (1). Effective dose was estimated by different methods: (i) RANDO phantom associated with thermoluminescent dosimeters (6), metal oxide semiconductor field-effect transistor dosimeters (3), and optically stimulated luminescent dosimeters (1). (ii) Scanner outputs, namely computed tomography dose index (1) and dose area product (2). (iii) Monte Carlo simulations (2). Conclusion: CBCT of extremities, cervical spine, ears and paranasal sinuses was found to be a low-dose volumetric imaging technique. Effective doses varied significantly because of different exposure settings of CBCT-units and different dosimetry systems used to estimate dose

Emphysema severity index (ESI) associated with respiratory death in a large Swedish general population

Recently, it has been shown and validated that presence and severity of emphysema on computed tomography could be estimated by a novel spirometry based index, the emphysema severity index (ESI). However, the clinical relevance of the index has not been established. We conducted cox-regression analyses with adjustment for age, smoking, sex, forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) to study whether ESI was associated with all-cause, respiratory and non-respiratory 10-year mortality. Study population was all participants with acceptable spirometry from the Gott Åldrande i Skåne study, a Swedish general population aged 65–102 years old. ESI is expressed as a continuous numeric parameter on a scale ranging from 0 to 10. Out of the 4453 participants in the main study, 3974 was included in the final analysis. Higher age, higher ESI, lower FEV1 and male sex increased hazard of respiratory death. ESI was significantly correlated to respiratory death but not non-respiratory death, while high age, male sex and low FEV1 was associated with non-respiratory as well as respiratory death. Current smoking habits increased the hazard of respiratory death but did not reach significance (p 0.066) One unit increase in ESI increased hazard of all-cause death by 20% (p 0.0002) and hazard of respiratory death by 57% (p < 0.0001). The ESI is a novel clinical marker of emphysema severity that is associated with respiratory death specifically. Since it can be derived from standard spirometry there are potential benefits for clinical practice in terms of more individualised prognosis and treatment alternatives