Enhancement of photocatalytic activity of TIO2 for gaseous toluene removal by simple mechanical mixing with modified zeolite

A zeolite Y was modified by the microwave-assisted method (MWA) for generating mesopores and was mechanically mixed with TiO2 for photocatalytic application. The external surface area, which is represented to the mesopore, was significantly increased about 5 to 10 times in the modified zeolites compared to the parent zeolite. The catalysts were used to catalyze the gas phase photodegradation of toluene, a volatile organic compound (VOC). The photocatalytic activity and stability of the catalyst were improved when the mesoporous zeolite was presented. The mechanical mixture contained 30 wt%. TiO2 and 70 wt%. mesoporous zeolite showed the highest toluene removal efficiency

Quantitative analyses of chest CT imaging in bronchial and pulmonary parenchyma diseases related to lung function disturbances : an automated approach based on artificial intelligence

L'analyse quantitative des lésions pulmonaires en imagerie thoracique tomodensitométrique est désormais possible grâce aux outils d'aide au diagnostic (ou Computer-Aided Diagnosis - CAD). Cette approche permet également une meilleure compréhension des mécanismes physiopathologiques grâce aux outils d'intelligence artificielle et à l'approche radiomique. Dans le cadre de notre Thèse, nous avions pu appliquer les outils de CAD et d'intelligence artificielle (machine learning et deep-learning) pour construire des scores de sévérité dans la dyskinésie ciliaire primitive (DCP), prédire la survenue du dysfonctionnement chronique du greffon pulmonaire (DCGP) et la mortalité à court terme de la sclérodermie systémique (ScS). Nous avions également combiné les résultats de machine learning sur les deux types de filtre médiastinal et pulmonaire du scanner thoracique. Nous avions ainsi amélioré l'efficacité des algorithmes d'apprentissage profond sans avoir recours à la nouvelle acquisition de scanner irradié. En pratique, nous avions montré que la caractéristique radiomique de premier ordre autorise la quantification automatique des modifications des images et l'établissement d'un score objectif pour évaluer la gravité de la DCP. L'analyse préliminaire de la combinaison des caractéristiques de densité en histogramme et de texture de la radiomique des scanners inspiratoires pourrait également prédire les modifications fonctionnelles respiratoires de la DCGP 6 mois avant leur survenue. Mais les résultats de notre étude préliminaire, n'étant pas statistiquement significatifs, nécessitent des études à plus grande échelle passant par des collaborations multicentriques. Pour la ScS, l'algorithme de « deep-learning » AtlasNet permet la quantification des lésions du parenchyme pulmonaire et la prédiction de la mortalité à court terme. Dans le dernier chapitre de notre thèse, nous rapportons l'influence du choix de filtre de reconstruction en TDM sur l'entrainement des modèles de machine learning pour la segmentation des zones lésionnelles pulmonaires interstitielles dans la ScS et la COVID-19, permettant ainsi d'augmenter la performance des modèles combinant les deux filtres de reconstruction.Quantitative analyses of structural lesions in thoracic tomodensitometric imaging of airways and lung parenchyma are now possible with computer-aided diagnostic tools (CAD). Underlying physiopathological mechanisms can also be better deciphered using artificial intelligence tools and the radiomic approach. In this PhD thesis, we have applied CAD, machine learning and deep-learning tools to build severity scores for primary ciliary dyskinesia (PCD), and to predict the occurrence of chronic lung allograft dysfunction (CLAD) and short-term mortality rate of patients with systemic sclerosis (ScS). By combining the results of machine learning on the two types of mediastinal and pulmonary kernels of the chest scanner, we can improve the efficiency of machine learning algorithms without the need to acquire a new irradiated scanner. For example, we have shown that using first-order radiomic parameters can allow automatic quantification of changes in imaging and establishment of an objective score for the assessment of disease severity in patients with PCD. For CLAD, the preliminary analysis of the combination of the density characteristics in histogram and the texture of the radiomics of the inspiratory scans could predict 6 months ahead of the occurrence of lung function tests changes. Our preliminary results however did not reach statistical significance and require larger studies with multicentre collaborations. For ScS, the AtlasNet deep-learning algorithm allows quantification of lung parenchyma lesions to help predict short-term mortality. In the last chapter of this thesis, we report the influence of the choice of reconstruction kernels in CT on the training of machine learning models in the process of segmentation of pathologic areas of interstitial lung diseases of ScS and COVID-19. This will hopefully offer the possibility to improve the performance of models in combinations of two reconstruction kernels

Analyses quantitatives de l'imagerie thoracique tomodensitométrique dans des maladies bronchiques et du parenchyme pulmonaire en relation avec des troubles fonctionnels respiratoires : approches automatisées basées sur l'intelligence artificielle

Quantitative analyses of structural lesions in thoracic tomodensitometric imaging of airways and lung parenchyma are now possible with computer-aided diagnostic tools (CAD). Underlying physiopathological mechanisms can also be better deciphered using artificial intelligence tools and the radiomic approach. In this PhD thesis, we have applied CAD, machine learning and deep-learning tools to build severity scores for primary ciliary dyskinesia (PCD), and to predict the occurrence of chronic lung allograft dysfunction (CLAD) and short-term mortality rate of patients with systemic sclerosis (ScS). By combining the results of machine learning on the two types of mediastinal and pulmonary kernels of the chest scanner, we can improve the efficiency of machine learning algorithms without the need to acquire a new irradiated scanner. For example, we have shown that using first-order radiomic parameters can allow automatic quantification of changes in imaging and establishment of an objective score for the assessment of disease severity in patients with PCD. For CLAD, the preliminary analysis of the combination of the density characteristics in histogram and the texture of the radiomics of the inspiratory scans could predict 6 months ahead of the occurrence of lung function tests changes. Our preliminary results however did not reach statistical significance and require larger studies with multicentre collaborations. For ScS, the AtlasNet deep-learning algorithm allows quantification of lung parenchyma lesions to help predict short-term mortality. In the last chapter of this thesis, we report the influence of the choice of reconstruction kernels in CT on the training of machine learning models in the process of segmentation of pathologic areas of interstitial lung diseases of ScS and COVID-19. This will hopefully offer the possibility to improve the performance of models in combinations of two reconstruction kernels.L'analyse quantitative des lésions pulmonaires en imagerie thoracique tomodensitométrique est désormais possible grâce aux outils d'aide au diagnostic (ou Computer-Aided Diagnosis - CAD). Cette approche permet également une meilleure compréhension des mécanismes physiopathologiques grâce aux outils d'intelligence artificielle et à l'approche radiomique. Dans le cadre de notre Thèse, nous avions pu appliquer les outils de CAD et d'intelligence artificielle (machine learning et deep-learning) pour construire des scores de sévérité dans la dyskinésie ciliaire primitive (DCP), prédire la survenue du dysfonctionnement chronique du greffon pulmonaire (DCGP) et la mortalité à court terme de la sclérodermie systémique (ScS). Nous avions également combiné les résultats de machine learning sur les deux types de filtre médiastinal et pulmonaire du scanner thoracique. Nous avions ainsi amélioré l'efficacité des algorithmes d'apprentissage profond sans avoir recours à la nouvelle acquisition de scanner irradié. En pratique, nous avions montré que la caractéristique radiomique de premier ordre autorise la quantification automatique des modifications des images et l'établissement d'un score objectif pour évaluer la gravité de la DCP. L'analyse préliminaire de la combinaison des caractéristiques de densité en histogramme et de texture de la radiomique des scanners inspiratoires pourrait également prédire les modifications fonctionnelles respiratoires de la DCGP 6 mois avant leur survenue. Mais les résultats de notre étude préliminaire, n'étant pas statistiquement significatifs, nécessitent des études à plus grande échelle passant par des collaborations multicentriques. Pour la ScS, l'algorithme de « deep-learning » AtlasNet permet la quantification des lésions du parenchyme pulmonaire et la prédiction de la mortalité à court terme. Dans le dernier chapitre de notre thèse, nous rapportons l'influence du choix de filtre de reconstruction en TDM sur l'entrainement des modèles de machine learning pour la segmentation des zones lésionnelles pulmonaires interstitielles dans la ScS et la COVID-19, permettant ainsi d'augmenter la performance des modèles combinant les deux filtres de reconstruction

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

International audienc

Total Psoas Area and Total Muscular Parietal Area Affect Long-Term Survival of Patients Undergoing Pneumonectomy for Non-Small Cell Lung Cancer

There is no standardization in methods to assess sarcopenia; in particular the prognostic significance of muscular fatty infiltration in lung cancer patients undergoing surgery has not been evaluated so far. We thus performed several computed tomography (CT)-based morphometric measurements of sarcopenia in 238 consecutive non-small cell lung-cancer patients undergoing pneumonectomy from 1 January 2007 to 31 December 2015. Sarcopenia was assessed by the following CT-based parameters: cross-sectional total psoas area (TPA), cross-sectional total muscle area (TMA), and total parietal muscle area (TPMA), defined as TMA without TPA. Measures were performed at the level of the third lumbar vertebra and were obtained for the entire muscle surface, as well as by excluding fatty infiltration based on CT attenuation. Findings were stratified for gender, and a threshold of the 33rd percentile was set to define sarcopenia. Furthermore, we assessed the possibility of being sarcopenic at both the TPA and TPMA level, or not, by taking into account of not fatty infiltration. Five-year survival was 39.1% for the whole population. Lower TPA, TMA, and TPA were associated with lower survival at univariate analysis; taking into account muscular fatty infiltration did not result in more powerful discrimination. Being sarcopenic at both psoas and parietal muscle level had the optimum discriminating power. At the multivariable analysis, being sarcopenic at both psoas and parietal muscles (considering the whole muscle areas, including muscular fat), male sex, increasing age, and tumor stage, as well as Charlson Comorbidity Index (CCI), were independently associated with worse long-term outcomes. We conclude that sarcopenia is a powerful negative prognostic factor in patients with lung cancer treated by pneumonectomy

Artificial intelligence in lung cancer: current applications and perspectives

International audienc

Sarcopenia as independent risk factor of postpneumonectomy respiratory failure, ARDS and mortality

International audienc

Automated computed tomographic scoring of lung disease in adults with primary ciliary dyskinesia

Abstract Background The present study aimed to develop an automated computed tomography (CT) score based on the CT quantification of high-attenuating lung structures, in order to provide a quantitative assessment of lung structural abnormalities in patients with Primary Ciliary Dyskinesia (PCD). Methods Adult (≥18 years) PCD patients who underwent both chest CT and spirometry within a 6-month period were retrospectively included. Commercially available lung segmentation software was used to isolate the lungs from the mediastinum and chest wall and obtain histograms of lung density. CT-density scores were calculated using fixed and adapted thresholds based on various combinations of histogram characteristics, such as mean lung density (MLD), skewness, and standard deviation (SD). Additionally, visual scoring using the Bhalla score was performed by 2 independent radiologists. Correlations between CT scores, forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were evaluated. Results Sixty-two adult patients with PCD were included. Of all histogram characteristics, those showing good positive or negative correlations to both FEV1 and FVC were SD (R = − 0.63 and − 0.67; p < 0.001) and Skewness (R = 0.67 and 0.67; p < 0.001). Among all evaluated thresholds, the CT-density score based on MLD + 1SD provided the best negative correlation with both FEV1 (R = − 0.68; p < 0.001) and FVC (R = − 0.71; p < 0.001), close to the correlations of the visual score (R = − 0.60; p < 0.001 for FEV1 and R = − 0.62; p < 0.001, for FVC). Conclusions Automated CT scoring of lung structural abnormalities lung in primary ciliary dyskinesia is feasible and may prove useful for evaluation of disease severity in the clinic and in clinical trials

Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT

International audienceIn patients with systemic sclerosis, a deep learning classifier applied to elastic registration of chest CT images depicted lung shrinkage and functional worsening with high accuracy.BackgroundLongitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage.PurposeTo develop a deep learning–based method to depict worsening of ILD based on lung shrinkage detection from elastic registration of chest CT scans in patients with systemic sclerosis (SSc).Materials and MethodsPatients with SSc evaluated between January 2009 and October 2017 who had undergone at least two unenhanced supine CT scans of the chest and pulmonary function tests (PFTs) performed within 3 months were retrospectively included. Morphologic changes on CT scans were visually assessed by two observers and categorized as showing improvement, stability, or worsening of ILD. Elastic registration between baseline and follow-up CT images was performed to obtain deformation maps of the whole lung. Jacobian determinants calculated from the deformation maps were given as input to a deep learning–based classifier to depict morphologic and functional worsening. For this purpose, the set was randomly split into training, validation, and test sets. Correlations between mean Jacobian values and changes in PFT measurements were evaluated with the Spearman correlation.ResultsA total of 212 patients (median age, 53 years; interquartile range, 45–62 years; 177 women) were included as follows: 138 for the training set (65%), 34 for the validation set (16%), and 40 for the test set (21%). Jacobian maps demonstrated lung parenchyma shrinkage of the posterior lung bases in patients found to have worsened ILD at visual assessment. The classifier detected morphologic and functional worsening with an accuracy of 80% (32 of 40 patients; 95% confidence interval [CI]: 64%, 91%) and 83% (33 of 40 patients; 95% CI: 67%, 93%), respectively. Jacobian values correlated with changes in forced vital capacity (R = −0.38; 95% CI: −0.25, −0.49; P < .001) and diffusing capacity for carbon monoxide (R = −0.42; 95% CI: −0.27, −0.54; P < .001).ConclusionElastic registration of CT scans combined with a deep learning classifier aided in the diagnosis of morphologic and functional worsening of interstitial lung disease in patients with systemic sclerosis

Usefulness of Hospital Admission Chest X-ray Score for Predicting Mortality and ICU Admission in COVID-19 Patients

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 during the 2021 peak pandemic in HCM City, Vietnam. CXR and clinical data were collected from Vinmec Central Park-hospitalized patients from July to September 2021. Three radiologists independently assessed the day-one CXR score consisting of both severity and extent of lung lesions (maximum score = 24). Among 219 included patients, 28 died and 34 were admitted to the ICU. There was a high consensus for CXR scoring among radiologists (κ = 0.90; CI95%: 0.89–0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85 CI95% 0.69–1) within the first 3 weeks after admission. A multivariate model confirmed a significant effect of an increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allowed for predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89)), respectively, and can be used to identify high-risk patients in needy countries such as Vietnam