Interstitial Lung Disease Patterns Research Articles

Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.

Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT). We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially. Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models. The success of the VGG16 model in the test phase was 95.02% accuracy. Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model. Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

HIGH-RESOLUTION COMPUTED TOMOGRAPHY IN DIAGNOSING AND MONITORING VARIOUS INTERSTITIAL LUNG DISEASES

Objective: Interstitial lung diseases (ILDs) are difficult to diagnose and require accurate imaging methods. The purpose of this work is to investigate ILD patterns and their relationships to pulmonary function using high-resolution computed tomography (HRCT). The aim is to augment our comprehension of ILDs, thereby facilitating customized approaches to diagnosis and treatment Methods: We recruited 50 ILD patients with radiological and clinical issues for a single-center trial. Spirometric data, symptoms, and demographics were recorded on comprehensive patient proformas. An expert radiologist used a Siemens-Somatom 6-slice CT scanner to analyze the HRCT. Pulmonary function indices were obtained using spirometry, which was carried out using a Medisoft Spiro Air spirometer. Results: In fifty cases with ILD, common features on HRCT were uneven pleural borders, ground glass opacities, and septal/subpleural lines. The complex interaction between radiological symptoms and respiratory health was highlighted by the substantial correlations seen between HRCT severities; extent scores, and reduced pulmonary function. Conclusion: The study reinforces the necessity for individualized diagnostic and treatment methods in the ILD respiratory landscape by providing detailed insights into their disease patterns and relationships.

Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention.

In patients with interstitial lung disease (ILD), accurate pattern assessment from their computed tomography (CT) images could help track lung abnormalities and evaluate treatment efficacy. Based on excellent image classification performance, convolutional neural networks (CNNs) have been massively investigated for classifying and labeling pathological patterns in the CT images of ILD patients. However, previous studies rarely considered the three-dimensional (3D) structure of the pathological patterns of ILD and used two-dimensional network input. In addition, ResNet-based networks such as SE-ResNet and ResNeXt with high classification performance have not been used for pattern classification of ILD. This study proposed a SE-ResNeXt-SA-18 for classifying pathological patterns of ILD. The SE-ResNeXt-SA-18 integrated the multipath design of the ResNeXt and the feature weighting of the squeeze-and-excitation network with split attention. The classification performance of the SE-ResNeXt-SA-18 was compared with the ResNet-18 and SE-ResNeXt-18. The influence of the input patch size on classification performance was also evaluated. Results show that the classification accuracy was increased with the increase of the patch size. With a 32 × 32 × 16 input, the SE-ResNeXt-SA-18 presented the highest performance with average accuracy, sensitivity, and specificity of 0.991, 0.979, and 0.994. High-weight regions in the class activation maps of the SE-ResNeXt-SA-18 also matched the specific pattern features. In comparison, the performance of the SE-ResNeXt-SA-18 is superior to the previously reported CNNs in classifying the ILD patterns. We concluded that the SE-ResNeXt-SA-18 could help track or monitor the progress of ILD through accuracy pattern classification.

Clinical Characteristics and Outcomes of Interstitial Lung Disease in Primary Sjögren's Syndrome: A Retrospective Cohort Study.

To describe the characteristics of primary Sjögren's syndrome (pSS) patients with interstitial lung disease (ILD) and to assess treatment response. All patients of pSS from 2010 to 2019 were retrospectively identified. Lung function tests, high resolution computed tomography (HRCT) findings, and treatment outcomes were analysed. Out of 550 patients with pSS, ILD was detected in 33 patients (frequency of 6 %). The mean(±SD) age at the diagnosis of pSS was 50 (± 9.3) years. 28/33(84.8%) were females. ILD onset preceded pSS diagnosis in 2 (6%) patients, simultaneously diagnosed in 21 (63.6%) patients and developed after pSS onset in 10 (30.3%) patients. 5 patients (15.15 %) were asymptomatic for ILD. Non-specific interstitial pneumonia (NSIP) accounted for the most frequent ILD subtype, in 15 patients (45.5%). Mycophenolate mofetil (MMF) was the most frequently used steroid sparing agent, in 25 patients (75.7%). 7 patients were lost to follow up. Response was seen in 22 patients, whereas 3 patients were non responders. There was one mortality due to lower respiratory tract infection-related sepsis. Presence of sicca symptoms [91.5% vs 8.7% (p<0.001)], NSIP pattern of ILD [90% vs 10% (p = 0.002)], and absence of Raynaud's phenomenon [91.7% vs 8.3% (p<0.001)] were significantly associated with responder status when compared to non-responders. ILD in primary Sjögren's syndrome is not an uncommon entity, and immunosuppression with steroids along with steroid-sparing agents led to good clinical outcomes of ILD in a majority of the patients in our cohort.

RADIOLOGICAL EVALUATION OF INTERSTITIAL LUNG DISEASES ON HIGH-RESOLUTION COMPUTED TOMOGRAPHY IN TERTIARY CARE CENTER OF NORTH INDIA

Objective: This study assessed the various high-resolution computed tomography (HRCT) patterns of interstitial lung diseases (ILDs) and to differentially diagnose ILD based on their clinical findings and distribution patterns. Methods: The study was conducted in the Department of Radiodiagnosis, Government Medical College and Rajindra Hospital, Patiala, Punjab, India. Forty-three patients with clinical suspicion of ILD were enrolled in the study. HRCT scans of the chest were done in all the cases taken in the study. Results: The mean age (±he age group of 51–60 years. We found slight female preponderance with females accounting for 53.5% and males accounting for 46.5% of the caseload. The most common presenting complaint was dyspnea on exertion (83.7%) followed by cough in 69.8%. The most frequent HRCT finding was septal thickening (90.7%), followed by tractional bronchiectasis (83.7%), fibrotic changes (72.1%), ground-glass opacities (65.1%), and honeycombing (58.1%). Based on HRCT findings, the most common HRCT pattern was the typical usual interstitial pneumonia pattern (58.1%), followed by the non-specific interstitial pneumonia pattern. The final diagnoses were made based on clinic-radiological findings and by the exclusion of other possibilities. The most common ILD reported was idiopathic pulmonary fibrosis (39.5%), followed by connective tissue disorder-ILD (20.9%), and SR-ILD (13.9%). This is followed by idiopathic non-specific interstitial pneumonia (11.6%), hypersensitivity pneumonitis (6.9%), and cryptogenic organizing pneumonia (2.3%). Conclusion: HRCT is a valuable technique for evaluating various ILDs even when chest X-rays are normal. It can differentially diagnose ILDs based on their clinical findings and distribution patterns.

Diagnosis of interstitial lung diseases: from Averill A. Liebow to artificial intelligence.

Histopathologic criteria of usual interstitial pneumonia (UIP)/idiopathic pulmonary fibrosis (IPF) were defined over the years and endorsed by leading organizations decades after Dr. Averill A. Liebow first coined the term UIP in the 1960s as a distinct pathologic pattern of fibrotic interstitial lung disease. Novel technology and recent research on interstitial lung diseases with genetic component shed light on molecular pathogenesis of UIP/IPF. Two antifibrotic agents introduced in the mid-2010s opened a new era of therapeutic approaches to UIP/IPF, albeit contentious issues regarding their efficacy, side effects, and costs. Recently, the concept of progressive pulmonary fibrosis was introduced to acknowledge additional types of progressive fibrosing interstitial lung diseases with the clinical and pathologic phenotypes comparable to those of UIP/IPF. Likewise, some authors have proposed a paradigm shift by considering UIP as a stand-alone diagnostic entity to encompass other fibrosing interstitial lung diseases that manifest a relentless progression as in IPF. These trends signal a pendulum moving toward the tendency of lumping diagnoses, which poses a risk of obscuring potentially important information crucial to both clinical and research purposes. Recent advances in whole slide imaging for digital pathology and artificial intelligence technology could offer an unprecedented opportunity to enhance histopathologic evaluation of interstitial lung diseases. However, current clinical practice trends of moving away from surgical lung biopsies in interstitial lung disease patients may become a limiting factor in this endeavor as it would be difficult to build a large histopathologic database with correlative clinical data required for artificial intelligence models.

Exploring Histologic Emphysema in a Rural Lobectomy Cohort: Insights and a Review Correlations of Histologic Emphysema

Exposure to cigarette smoking is extensive in rural Appalachia where one in four adults’ smokes. The clinical implications of this habit are evident among patients with some of the highest national rates for chronic obstructive pulmonary disease (COPD), lung cancer, and pulmonary fibrosis. Individuals undergoing surgical lung resection for suspicious lung nodules or masses at a major rural academic center in the area demonstrated an excessive burden of histologic emphysema (73.5%). This destructive process of the alveoli was linked to a significant burden of comorbid conditions, various radiologic patterns of interstitial lung diseases and interstitial lung abnormalities, histologic fibrosis, inflammatory processes (respiratory bronchitis, desquamative interstitial pneumonia, peribronchiolar metaplasia), anthracosis, and lung cancer. Physiologically, this combination of injuries imposed substantial limitations. Findings presented may enhance the understanding of concurrent changes occurring in the smoker. The complex inter-relationships and disparities between clinical COPD, radiologic and histologic emphysema are defined. While emphysema remains an irreversible pathology, associated inflammatory and fibrotic conditions are possibly amenable to earlier smoking cessation strategies and available disease-modifying therapies.

Interstitial lung disease following COVID-19 vaccination: a disproportionality analysis using the Global Scale Pharmacovigilance Database (VigiBase)

Background and objectiveDespite several case reports, population-based studies on interstitial lung disease (ILD) following COVID-19 vaccination are lacking. Given the unprecedented safety issue of COVID-19 vaccination, it is important to...

A Historic Cohort Analysis of Radiographic and Serologic Findings in Patients With Scleroderma and Interstitial Lung Disease.

Few studies have investigated associations between rheumatologic serology patterns and different interstitial lung disease (ILD) patterns. We present novel findings of a historic cohort study (n = 454) with data collected from 2011 to 2021 within our hospital system. In this institutional review board-approved study, data regarding rheumatologic serologies and ILD patterns were noted based on chart review in patients with scleroderma. The Kruskal-Wallis rank sum and χ2 tests were used for statistical analysis. Results showed a statistically significant association between anti-U1 snRNP with lymphoid interstitial pneumonia, which has not been previously described. We demonstrated novel serologic associations with ILD patterns, which have important clinical implications. More robust and high-powered studies are needed to elucidate the role of serologic testing and their association with ILD phenotypes.

Clinical Characteristics and Prognostic Value of Ro52/SSA Antibodies in Idiopathic Inflammatory Myopathies.

Anti-Ro52 are myositis-associated antibodies found in idiopathic inflammatory myopathies (IIMs). This chart review aims to evaluate the frequency, significance, and associated clinical characteristics of Ro52/SSA positivity in IIM patients. We performed a chart review of IIM patients diagnosed between January 2006 and December 2020. All patients met either the 1975 Bohan and Peter or the European League Against Rheumatism/American College of Rheumatology classification criteria for probable or definite myositis. Demographics, clinical and serologic parameters, treatments, and outcomes were compared in patients with anti-Ro52/SSA antibodies and patients without anti-Ro52/SSA antibodies. One hundred eighty-nine patients with IIM were tested for either Ro52 or SSA, with 45 positive for Ro52/SSA (23.8%). Patients with IIM and Ro52/SSA+ were younger at age at onset of disease (44.8 vs. 51.2 years, p = 0.008). Ro52/SSA+ was more common in antisynthetase syndrome (p < 0.001; odds ratio [OR], 4.44; 95% confidence interval [CI], 2.11-9.33) and not frequently identified in clinically amyopathic dermatomyositis (CADM) (p = 0.02; OR, 0.13; 95% CI, 0.02-0.96) or immune-mediated necrotizing myopathy (p = 0.003; OR, 0.14; 95% CI, 0.03-0.63). Of the extraskeletal muscle manifestations, interstitial lung disease (ILD) was strongly associated with Ro52/SSA+ (p < 0.001; OR, 6.61; 95% CI, 3.15-13.86), as was dysphagia (p = 0.006; OR, 2.73; 95% CI, 1.31-5.71). Interstitial lung disease pattern and pulmonary function testing impairment did not differ based on antibody status. There was no significant difference in outcomes between groups. In this myositis cohort, Ro52/SSA+ was present in nearly one-fourth of the population and had a strong association with the antisynthetase syndrome subtype, ILD, and dysphagia. Although these disease manifestations are associated with significant morbidity, in our cohort, they were not associated with increased mortality or more severe ILD.

CT Patterns of Interstitial Lung Disease in Patients with Plaque Psoriasis: A Retrospective Case Series Study.

Background and Objectives: Recently published articles reported an association between psoriasis and interstitial lung diseases (ILDs). The aim of this study is to evaluate the differences in ILD computed tomography (CT) patterns between smoker and never smoker plaque psoriasis (PP) patients under topical treatment without psoriatic arthritis (PA), inflammatory bowel disease (IBD) or connective tissue diseases (CTDs). Matherials and Methods: Two radiologists evaluated chest CT examinations of 65 patients (33 smokers, 32 never smokers) with PP. Results: Usual interstitial pneumonia (UIP) pattern was diagnosed in 36 patients, nonspecific interstitial pneumonia pattern in 19, hypersensitivity pneumonitis in 7 and pleuropulmonary fibroelastosis (PPFE) in 3 patients. UIP pattern showed a statistically significant higher frequency in smoker patients (p = 0.0351). Respiratory symptoms were reported in 80% of patients. Conclusions: ILDs seems to represent a new comorbidity associated with psoriasis. Moreover, a statistically significant association between smokers and UIP pattern in PP patients is found. Respiratory symptoms should be evaluated in PP patients, in collaboration with a radiologist and a pneumologist. However, further studies are required to better understand the epidemiology of ILDs in PP patients.

Successful management of interstitial lung disease in dermatomyositis complicated by malignancy: a case-based review.

Dermatomyositis (DM) is associated with interstitial lung disease (ILD) and malignancy. However, the coexistence of ILD and malignancy (DM-ILD-malignancy) is rare, and limited information exists regarding its management. Herein, we report the case of a 70-year-old man who developed DM with rapidly progressive ILD and advanced gastric cancer and provide a literature review of managing DM-ILD-malignancy. The patient presented with typical DM skin rashes and shortness of breath, which worsened within 1month, without muscular symptoms. Additionally, the patient tested negative for myositis-specific autoantibodies (MSAs). Computed tomography revealed ILD and advanced gastric cancer, which was confirmed on endoscopic examination to be a poorly differentiated adenocarcinoma. Although the patient's ILD progressed rapidly, surgical treatment of the cancer was prioritized. Prednisolone (PSL) 0.5mg/kg was initiated 3days before surgery and increased to 1mg/kg at 7days postoperative. Remarkable improvement in the skin rash and ILD was observed, and the PSL dose was tapered without immunosuppressants. A literature review revealed that anti-melanoma differentiation-associated gene 5 and anti-aminoacyl transfer RNA synthetase antibodies are the predominant MSAs in DM-ILD-malignancy, and the optimal treatment should be determined based on several factors, including ILD patterns, and malignancy type and stage. In particular, lung cancer may be a risk factor for the acute exacerbation of ILD, and preceding immunosuppression would be useful. Furthermore, prioritizing surgery for gastric cancer is effective because of its paraneoplastic nature.

Classification of Interstitial lung disease patterns in CT images using convolutional neural network

Abstract-Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2x2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micro nodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 1469 image patches, derived by 20 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. The classification performance demonstrated the potential of CNNs in analysing lung patterns. Key Words: Micro nodules, honeycombing, image analysis, classification

Pulmonary involvement in idiopathic inflammatory myopathies

Idiopathic inflammatory myopathies are rare systemic diseases with different types of pulmonary manifestations depending on the underlying aetiology; here, interstitial lung diseases (ILD) are the most frequently found patterns depending on the underlying disorder. There is a lack of sufficient prospective studies on this heterogeneous group of patients, particularly in case of ILD being involved. The diagnosis is based upon guideline recommendations for ILD and requires a multidisciplinary discussion within a team with specific expertise in this field. Myositis specific antibodies and myositis associated antibodies form an essential part of the diagnostic tools and may also be associated with a certain phenotype or disease progression. Anti-t-RNA-synthetase antibodies (Anti-ARS) and anti-melanoma differentiation-associated gene 5 antibodies (MDA5) play an important clinical role for treatment the estimation of response and prognosis. The most common ILD patterns are nonspecific interstitial pneumonia (NSIP) and organising pneumonia (OP) or a mixed pattern of both. Treatment is based on systemic steroids and early initiation of other immunosuppressant drugs. Evidence for this is, however, sparse, since most of the studies having investigated treatment modalities are of retrospective nature, even though some new prospective data may be useful for the establishment of treatment pathways in the future.

Ways to predict interstitial lung disease in patients with systemic sclerosis: results of an observational study

In patients with systemic sclerosis (SSc), interstitial lung disease (ILD) is a factor in the decline of functional capacity up to disability and is also the leading cause of death. Therefore, one of the most important tasks in the treatment of this group of patients is not only to detect involvement of respiratory system, but also to predict the likelihood of its development.Objective: to study the possibility of predicting the development of ILD and advanced ILD in patients with SSc.Material and methods. The study included 79 patients with SSc (mean age 64.4±11.5 years; 94.9% women) from the Registry of myositis, SSc and Mixed Connective Tissue Diseases (РЕМИССиС) who underwent high-resolution computed tomography (HRCT) of the lungs. Classification trees (CTr) were constructed to predict the development of widespread ILD using the CHAID algorithm (exhaustive). All patients were tested for antibodies against Scl-70 (anti-Scl-70), CENP-B (anti-CENP-B), and PmScl (anti-PmScl).Results and discussion. ILD signs according to HRCT were detected in 53 patients. Fibrotic (34.2%) and cellular (15.2%) types of nonspecific interstitial pneumonia were the most common, and common interstitial pneumonia was less frequent (11.4%).The presence of ILD and advanced ILD (involvement of more than 20% of the lung parenchyma) were significantly associated with the detection of any autoantibodies, except anti-centromere antibodies, an increase in pulmonary artery systolic pressure, a decrease in forced vital capacity, diffusing capacity of the lungs, blood oxygen saturation at rest, and all parameters of six-minute walk test (6MWT), and complaints of shortness of breath. In addition, the presence of extensive ILD was also significantly associated with diffuse SSc and with SSc without skin manifestations.In establishing the CTr, it was found that the development of widespread ILD was unlikely in individuals who were able to walk more than 440 m in 6MWT and had neither anti-Scl-70 nor anti-PmScl.Significant associations were also found between the radiological pattern of ILD and the types of disease-specific antibodies.Conclusion. The 6MWT data in conjunction with the results of testing for SSc-specific autoantibodies provide a very accurate prediction of the presence and extent of ILD. It is advisable to include these indicators in the algorithm for screening and monitoring patients with SSc.

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CTand determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels. Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method. A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA[-0.4, 0.4]). Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.

Semi-Supervised Segmentation of Interstitial Lung Disease Patterns from CT Images via Self-Training with Selective Re-Training.

Accurate segmentation of interstitial lung disease (ILD) patterns from computed tomography (CT) images is an essential prerequisite to treatment and follow-up. However, it is highly time-consuming for radiologists to pixel-by-pixel segment ILD patterns from CT scans with hundreds of slices. Consequently, it is hard to obtain large amounts of well-annotated data, which poses a huge challenge for data-driven deep learning-based methods. To alleviate this problem, we propose an end-to-end semi-supervised learning framework for the segmentation of ILD patterns (ESSegILD) from CT images via self-training with selective re-training. The proposed ESSegILD model is trained using a large CT dataset with slice-wise sparse annotations, i.e., only labeling a few slices in each CT volume with ILD patterns. Specifically, we adopt a popular semi-supervised framework, i.e., Mean-Teacher, that consists of a teacher model and a student model and uses consistency regularization to encourage consistent outputs from the two models under different perturbations. Furthermore, we propose introducing the latest self-training technique with a selective re-training strategy to select reliable pseudo-labels generated by the teacher model, which are used to expand training samples to promote the student model during iterative training. By leveraging consistency regularization and self-training with selective re-training, our proposed ESSegILD can effectively utilize unlabeled data from a partially annotated dataset to progressively improve the segmentation performance. Experiments are conducted on a dataset of 67 pneumonia patients with incomplete annotations containing over 11,000 CT images with eight different lung patterns of ILDs, with the results indicating that our proposed method is superior to the state-of-the-art methods.

Patterns of Interstitial Lung Disease in Egyptian Patients with Systemic Sclerosis: Relation to Disease Parameters.

Pulmonary involvement is the most common leading cause of morbidity and mortality associated with systemic sclerosis. Therefore, identifying the various patterns of pulmonary affection is crucial in the clinical management of these patients. In the current study, we aim to investigate the patterns of interstitial lung disease (ILD) associated with SSc patients (SSc- ILD) and their relation to serologic markers and clinical parameters. A cross-sectional study was undertaken on thirty-four adult SSc patients who met the 2013 ACR/EULAR criteria for SSc and Forty healthy controls of matched age and sex. The patients were subjected to history taking, clinical examination, skin assessment using the modified Rodnan Skin Score (mRSS), chest x-ray (CXR), pulmonary function test (PFTs), and high resolution computed tomography of the chest (HRCT). Routine laboratory tests were conducted in addition to immunologic tests and an enzyme-linked immunosorbent assay (ELISA) to determine the IL-33 level. ILD was found in 23 SSc patients (67.6%); 20 patients had diffuse type while 3 patients had limited type. Non-specific interstitial pneumonia (NSIP) was found in 56.5%, usual interstitial pneumonia (UIP) was found in 21.7%, pleuroparenchymal fibroelastosis (PPFE) was found in 8.7%, and organizing pneumonia (OP) with the mixed pattern was found in 13% of SSc patients. Additionally, the mean IL-33 level in SSc patients was 98±12.7 compared to 66.2±10.6 in the control group (p < 0.001), with ILD patients having a significantly higher level (101.7±13.4) than those without (90.4±6.2), and a strong positive correlation with mRSS. Even in asymptomatic patients with SSc, ILD is prevalent, with NSIP being the most common pattern. IL-33 could be considered a potential biomarker for predicting the presence of ILD in SSc patients.

Ultra-high resolution CT imaging of interstitial lung disease: impact of photon-counting CT in 112 patients.

To compare lung parenchyma analysis on ultra-high resolution (UHR) images of a photon-counting CT (PCCT) scanner with that of high-resolution (HR) images of an energy-integrating detector CT (EID-CT). A total of 112 patients with stable interstitial lung disease (ILD) were investigated (a) at T0 with HRCT on a 3rd-generation dual-source CT scanner; (b) at T1 with UHR on a PCCT scanner; (c) with a comparison of 1-mm-thick lung images. Despite a higher level of objective noise at T1 (74.1 ± 14.1 UH vs 38.1 ± 8.7UH; p < 0.0001), higher qualitative scores were observed at T1 with (a) visualization of more distal bronchial divisions (median order; Q1-Q3) (T1: 10th division [9-10]; T0: 9th division [8-9]; p < 0.0001); (b) greater scores of sharpness of bronchial walls (p < 0.0001) and right major fissure (p < 0.0001). The scores of visualization of CT features of ILD were significantly superior at T1 (micronodules: p = 0.03; linear opacities, intralobular reticulation, bronchiectasis, bronchiolectasis, and honeycombing: p < 0.0001), leading to the reclassification of 4 patients with non-fibrotic ILD at T0, recognized with fibrotic ILD at T1. At T1, the mean (± SD) radiation dose (CTDI vol: 2.7 ± 0.5mGy; DLP: 88.5 ± 21mGy.cm) was significantly lower than that delivered at T0 (CTDI vol: 3.6 ± 0.9mGy; DLP: 129.8 ± 31.7mGy.cm) (p < 0.0001), corresponding to a mean reduction of 27% and 32% for the CTDIvol and DLP, respectively. The UHR scanning mode of PCCT allowed a more precise depiction of CT features of ILDs and reclassification of ILD patterns with significant radiation dose reduction. Evaluation of lung parenchymal structures with ultra-high-resolution makes subtle changes at the level of the secondary pulmonary lobules and lung microcirculation becoming visually accessible, opening new options for synergistic collaborations between highly-detailed morphology and artificial intelligence. • Photon-counting CT (PCCT) provides a more precise analysis of lung parenchymal structures and CT features of interstitial lung diseases (ILDs). • The UHR mode ensures a more precise delineation of fine fibrotic abnormalities with the potential of modifying the categorization of ILD patterns. • Better image quality at a lower radiation dose with PCCT opens new horizons for further dose reduction in noncontrast UHR examinations.

Comparison of the diagnostic criteria for progressive pulmonary fibrosis in connective tissue disease related interstitial lung disease

BackgroundProgressive pulmonary fibrosis (PPF) is possible among patients with connective tissue disease (CTD) related interstitial lung disease (ILD). Our aim herein was to compare the prevalence and clinical characteristics of patients with CTD-ILD, with and without PPF, according to the different diagnostic criteria currently used in practice. MethodsThis retrospective study included patients diagnosed with CTD-ILD, with a ≥1-year follow-up of their lung function, at a single tertiary hospital in South Korea. Diagnostic criteria from two clinical trials (RELIEF and TRAIL1) and from a recently updated guideline (ATS/ERS/JRS/ALAT) were applied. ResultsOf the 107 patients included, 80% tested positive for Sjogren's disease, rheumatoid arthritis, and systemic sclerosis. The prevalence of CTD-ILD with PPF for the different diagnostic criteria was as follows: RELIEF, 25.2%; TRAIL1, 20.6%; and ATS/ERS/JRS/ALAT, 38.3%. More previous history of pulmonary tuberculosis and less positivity for antinuclear antibodies were identified in the PPF group. The radiologic pattern of ILD did not differ between patients with and without PPF, with a usual interstitial pneumonia pattern identified in 34.6% of the patients. Systemic steroids and immunomodulatory agents were used in about 80% of patients with PPF and 50% without PPF, irrespective of the diagnostic criteria used. Antifibrotic therapy was used in a limited number of patients in both groups. ConclusionsThe proportion of patients with CTD-ILD and PPF was higher for the ATS/ERS/JRS/ALAT guideline criteria, without a between-group difference in clinical characteristics.