Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

PURPOSE

Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images.

MATERIALS AND METHODS

A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient.

RESULTS

The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm's categorisation was a significant predictor of IPF progression.

CONCLUSIONS

The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease

PURPOSE

To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest.

MATERIALS AND METHODS

Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results. GAP I, II, and III have 137, 36, and 11 cases, respectively. The cases in GAP Ⅱ and Ⅲ were then combined into one group, and the two groups of patients were randomly divided into the training and testing groups with a 7:3 ratio. The radiomics features were extracted using AK software. Multivariate logistic regression analysis was then conducted to establish a radiomics model. A nomogram model was established on the basis of Rad-score and clinical factors (age and gender).

RESULTS

For the radiomics model, four significant radiomics features were selected to construct the model and showed excellent ability to differentiate GAP I from GAP Ⅱ and Ⅲ in both the training group (the area under the curve [AUC] = 0.803, 95% confidence interval [CI]: 0.724-0.874) and testing group (AUC = 0.801, 95% CI:0.663-0.912). The nomogram model that combined clinical factors and radiomics features improved higher accuracy of both training (88.4% vs. 82.1%) and testing (83.3% vs. 79.2%).

CONCLUSION

The disease severity assessment of patients with CTD-ILD can be evaluated by applying the radiomics method based on CT images. The nomogram model demonstrates better performance for predicting the GAP staging.

Diagnostic finding on high-resolution computed tomography (HRCT) predicts a good response to pirfenidone in patients with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a debilitating condition, with a life expectancy of 2 to 5 years after diagnosis. Pirfenidone is a drug that has been shown to reduce the decline in forced vital capacity (FVC). We sought to identify whether different patterns on high-resolution computed tomography (HRCT) have different clinical effects through a retrospective comparison of baseline values and changes in pulmonary function tests (PFTs) after treatment with pirfenidone. We retrospectively analyzed data from IPF patients treated with pirfenidone at Nanjing Drum Tower Hospital in Jiangsu Province, China. According to the HRCT pattern, the patients were divided into usual interstitial pneumonitis (UIP) and possible UIP groups. Baseline clinical characteristics and changes every 6 months in the PFTs during the follow-up period were compared between the 2 groups. A total of 65 consecutive patients were enrolled. According to the HRCT pattern, patients were clustered into the UIP group (n = 46) and possible UIP group (n = 19). No difference was observed in the baseline PFTs ratio between the 2 groups. The FVC values of the 2 groups were not significantly different at the initial treatment and at 6 and 12 months after pirfenidone treatment (P = .081, 0.099, and 0.236, respectively). The improvement in % diffusion capacity of the lung for carbon monoxide (%DLCO) was higher in the possible UIP group after 6 and 12 months of pirfenidone treatment (P = .149, 0.026, and 0.025, respectively). The annual decrease in FVC was not significantly different between the 2 groups, and the annual decrease in %DLCO in the UIP group was significantly higher than that in patients with the possible UIP type (-7.767 ± 12.797 vs 0.342 ± 20.358, P < .05). These results indicate that patients with IPF with a possible UIP pattern on HRCT showed indications of a good response to pirfenidone.

Three-dimensional ultrashort echo time magnetic resonance imaging in assessment of idiopathic pulmonary fibrosis, in comparison with high-resolution computed tomography

Background

We aimed to evaluate the image quality, feasibility, and diagnostic performance of three-dimensional ultrashort echo time magnetic resonance imaging (3D UTE-MRI) to assess idiopathic pulmonary fibrosis (IPF) compared with high-resolution computed tomography (HRCT) and half-Fourier single-shot turbo spin-echo (HASTE) MRI.

Methods

A total of 36 patients with IPF (34 men; mean age: 62±8 years, age range: 43 to 78 years) were prospectively included and underwent HRCT and chest MRI on the same day. Chest MRI was performed with a free-breathing 3D spiral UTE pulse sequence and HASTE sequence on a 1.5 T MRI. Two radiologists independently evaluated the image quality of the HRCT, HASTE, and 3D UTE-MRI. They assessed the representative imaging features of IPF, including honeycombing, reticulation, traction bronchiectasis, and ground-glass opacities. Image quality of the 3D UTE-MRI, HASTE, and HRCT were assessed using a 5-point visual scoring method. Kappa and weighted kappa analysis were used to measure intra- and inter-observer and inter-method agreements. Sensitivity (SE), specificity (SP), and accuracy (AC) were used to assess the performance of 3D UTE-MRI for detecting image features of IPF and monitoring the extent of pulmonary fibrosis. Linear regressions and Bland-Altman plots were generated to assess the correlation and agreement between the assessment of the extent of pulmonary fibrosis made by the 2 observers.

Results

The image quality of HRCT was higher than that of HASTE and UTE-MRI (HRCT vs. UTE-MRI vs. HASTE: 4.9±0.3 vs. 4.1±0.7 vs. 3.0±0.3; P<0.001). Interobserver agreement of HRCT, HASTE, and 3D UTE-MRI when assessing pulmonary fibrosis was substantial and excellent (HRCT: 0.727≤ κ ≤1, P<0.001; HASTE: 0.654≤ κ ≤1, P<0.001; 3D UTE-MRI: 0.719≤ κ ≤0.824, P<0.001). In addition, reticulation (SE: 97.1%; SP: 100%; AC: 97.2%; κ =0.654), honeycombing (SE: 83.3%; SP: 100%; AC: 86.1%; κ =0.625) patterns, and traction bronchiectasis (SE: 94.1%; SP: 100%; AC: 94.4%, κ =0.640) were also well-visualized on 3D UTE-MRI, which was significantly superior to HASTE. Compared with HRCT, the sensitivity of 3D UTE-MRI to detect signs of pulmonary fibrosis (n=35) was 97.2%. The interobserver agreement in elevation of the extent of pulmonary fibrosis with HRCT and 3D UTE-MRI was R²=0.84 (P<0.001) and R²=0.84 (P<0.001), respectively. The extent of pulmonary fibrosis assessed with 3D UTE-MRI [median =9, interquartile range (IQR): 6.25 to 10.00] was lower than that from HRCT (median =12, IQR: 9.25 to 13.00; U=320.00, P<0.001); however, they had a positive correlation (R=0.72, P<0.001).

Conclusions

As a radiation-free non-contrast enhanced imaging method, although the image quality of 3D UTE-MRI is inferior to that of HRCT, it has high reproducibility to identify the imaging features of IPF and evaluate the extent of pulmonary fibrosis.

Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases

OBJECTIVES

To compare the lung CT volume (CTvol) and pulmonary function tests in an interstitial lung disease (ILD) population. Then to evaluate the CTvol loss between idiopathic pulmonary fibrosis (IPF) and non-IPF and explore a prognostic value of annual CTvol loss in IPF.

METHODS

We conducted in an expert center a retrospective study between 2005 and 2018 on consecutive patients with ILD. CTvol was measured automatically using commercial software based on a deep learning algorithm. In the first group, Spearman correlation coefficients (r) between forced vital capacity (FVC), total lung capacity (TLC), and CTvol were calculated. In a second group, annual CTvol loss was calculated using linear regression analysis and compared with the Mann-Whitney test. In a last group of IPF patients, annual CTvol loss was calculated between baseline and 1-year CTs for investigating with the Youden index a prognostic value of major adverse event at 3 years. Univariate and log-rank tests were calculated.

RESULTS

In total, 560 patients (4610 CTs) were analyzed. For 1171 CTs, CTvol was correlated with FVC (r: 0.86) and TLC (r: 0.84) (p < 0.0001). In 408 patients (3332 CT), median annual CTvol loss was 155.7 mL in IPF versus 50.7 mL in non-IPF (p < 0.0001) over 5.03 years. In 73 IPF patients, a relative annual CTvol loss of 7.9% was associated with major adverse events (log-rank, p < 0.0001) in univariate analysis (p < 0.001).

CONCLUSIONS

Automated lung CT volume may be an alternative or a complementary biomarker to pulmonary function tests for the assessment of lung volume loss in ILD.

KEY POINTS

• There is a good correlation between lung CT volume and forced vital capacity, as well as for with total lung capacity measurements (r of 0.86 and 0.84 respectively, p < 0.0001). • Median annual CT volume loss is significantly higher in patients with idiopathic pulmonary fibrosis than in patients with other fibrotic interstitial lung diseases (155.7 versus 50.7 mL, p < 0.0001). • In idiopathic pulmonary fibrosis, a relative annual CT volume loss higher than 9.4% is associated with a significantly reduced mean survival time at 2.0 years versus 2.8 years (log-rank, p < 0.0001).

Fibro-CoSANet: pulmonary fibrosis prognosis prediction using a convolutional self attention network

Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung disease that causes lung function decline by lung tissue scarring. Although lung function decline is assessed by the forced vital capacity (FVC), determining the accurate progression of IPF remains a challenge. To address this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal learning based approach, to predict the FVC decline. Fibro-CoSANet utilized computed tomography images and demographic information in convolutional neural network frameworks with a stacked attention layer. Extensive experiments on the OSIC Pulmonary Fibrosis Progression Dataset demonstrated the superiority of our proposed Fibro-CoSANet by achieving new state-of-the-art modified Laplace log-likelihood score of -6.68. This network may benefit research areas concerned with designing networks to improve the prognostic accuracy of IPF. The source-code for Fibro-CoSANet is available at: https://github.com/zabir-nabil/Fibro-CoSANet.

Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images

Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images. More specifically, machine-driven design exploration was leveraged to determine a strong architectural design for CT lung analysis, upon which we build a customized network design tailored for predicting forced vital capacity (FVC) based on a patient's CT scan, initial spirometry measurement, and clinical metadata. Finally, we leverage an explainability-driven performance validation strategy to study the decision-making behavior of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis Progression Challenge showed that the proposed Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the challenge. Furthermore, explainability-driven performance validation demonstrated that the proposed Fibrosis-Net exhibits correct decision-making behavior by leveraging clinically-relevant visual indicators in CT images when making predictions on pulmonary fibrosis progress. Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behavior when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.

Viral hepatitis and interstitial lung diseases: can HRCT assess their relation and characterize its pattern?

Background

Clinical and pathological studies suggested the presence of a relation between active viral hepatitis and interstitial lung diseases (ILD) ranging from mild to moderate relation. Most studies attribute this relation to viral geno-physiological characteristics. The purpose of the study is first to assess the role of high-resolution computed tomography (HRCT) in evaluating the relation between viral hepatitis and ILDs, then to characterize the predominant pattern of ILD that affects patient management and prognosis.

Results

This prospective study was conducted on 300 patients with viral hepatitis. They were divided into three groups of patients according to blood viremia assessed by polymerase chain reaction (PCR) as well as the diffusing capacity of carbon monoxide (DlCO) in examined pulmonary function tests (PFT). Group [A] included 100 patients with low or moderate viremia and showing normal or low to moderate DlCO decline (> 50%). Group [B] included 100 patients with high viremia and showing normal or low to moderate DlCO decline (> 50%). Group [C] included 100 patients with high viremia and showing a restrictive DlCO decline pattern (< 50%). The study included 182 males and 118 females with a ratio of 3:2, while the age ranged between 40 and 70 years (mean age of 55 years). No ILD was found among the group [A] patient. Meanwhile, 27% of patients in the group [B] showed a non-fibrotic pattern of ILD and 50% of patients in the group [C] showed a fibrotic pattern of ILD. Among patients in group [B] and group [C] together, 77 patients showed ILD accounting for 38.5%.

Conclusion

In concordance with the results of the previous clinicopathological and geno-physiological studies, our HRCT results further established a mild to moderate relation between active hepatitis and ILD regardless of the pulmonary functions. The fibrotic pattern of ILD with poor response to therapy and poor prognosis was found in those patients with concomitant restrictive PFT and rapidly progressive symptoms.

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

Artificial intelligence (AI) is changing the landscape in medicine. AI-based applications will empower pulmonary specialists to seize modern practice and research opportunities. Data-driven precision medicine is already here.https://bit.ly/324tl2m

Complications in Idiopathic Pulmonary Fibrosis: Focus on Their Clinical and Radiological Features

Idiopathic pulmonary fibrosis (IPF) is a fibrotic lung disease with uncertain origins and pathogenesis; it represents the most common interstitial lung disease (ILD), associated with a pathological pattern of usual interstitial pneumonitis (UIP). This disease has a poor prognosis, having the most lethal prognosis among ILDs. In fact, the progressive fibrosis related to IPF could lead to the development of complications, such as acute exacerbation, lung cancer, infections, pneumothorax and pulmonary hypertension. Pneumologists, radiologists and pathologists play a key role in the identification of IPF disease, and in the characterization of its complications-which unfortunately increase disease mortality and reduce overall survival. The early identification of these complications is very important, and requires an integrated approach among specialists, in order to plane the correct treatment. In some cases, the degree of severity of patients having IPF complications may require a personalized approach, based on palliative care services. Therefore, in this paper, we have focused on clinical and radiological features of the complications that occurred in our IPF patients, providing a comprehensive and accurate pictorial essay for clinicians, radiologists and surgeons involved in their management.

Deep learning in interstitial lung disease-how long until daily practice

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease.

Key Points

• Deep learning algorithms are used in pattern recognition of different interstitial lung diseases.

• High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease.

• Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.

Impact of pulmonary interstitial lesions on efficacy and prognosis of EGFR-TKI-treated advanced non-small cell lung cancers

Background

This study aimed to assess the impact of pre-existing pulmonary interstitial lesions (PIL) on the efficacy and prognosis of patients with epidermal growth factor receptor (EGFR) mutant non-small cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitor (TKI).

Methods

Patients with advanced NSCLC harboring EGFR exon 19 deletion (E19 del) or exon 21 (E21) L858R were enrolled in this study. All patients underwent high resolution computed tomography (HRCT) chest scans prior to EGFR-TKI treatment. Pre-existing PIL was graded according to HRCT imaging (PIL 0, 1, 2, and 3). Cox proportional-hazards regression models were used to identify the prognostic factors for progression-free survival (PFS).

Results

A total of 134 eligible patients were enrolled. The overall objective response rate (ORR) and median PFS were 73.1% and 10.0 months (95% CI: 7.51–12.49), respectively. There were 62 (46.3%), 25 (18.7%), 28 (20.9%), and 19 (14.1%) cases of PIL grade 0, 1, 2, and 3, respectively, with median PFS and ORR of 12.9 months and 80.6%, 11.0 months and 72.0%, 10.0 months and 71.4%, and 7.0 months and 52.6%, respectively. Multivariate analysis showed that squamous cell carcinoma (vs. adenocarcinoma, HR =4.33), E21 L858R (vs. E19 del, HR =1.57), and PIL grade 3 (vs. grade 0–2, HR =1.60–2.48) were poor prognostic factors for PFS (P<0.05 for all).

Conclusions

Pre-existing PIL grade is an independent prognostic factor for predicting resistance to EGFR-TKIs in patients with EGFR-mutant advanced NSCLC. Higher PIL grade suggests higher risk of early progression.

Idiopathic pulmonary fibrosis: Significance of the usual interstitial pneumonia (UIP) CT-scan patterns defined in new international guidelines

INTRODUCTION

The new 2018 international guidelines for diagnosing usual interstitial pneumonia (UIP)/idiopathic pulmonary fibrosis (IPF) by CT scan split the old pattern possible UIP (2011 IPF guidelines) into two new patterns: probable UIP and indeterminate for UIP. However, the proportions and prognoses of these new CT-scan patterns are not clear.

METHODS

We used a monocentric retrospective cohort of 322 patients suspected of having IPF (University Hospital of Rennes; Competence Center for Rare Lung Diseases; 1 January 2012-31 December 2017). All patients initially diagnosed by CT scan as possible UIP were included. The chest CT-scans were then reclassified according to the new 2018 international guidelines by 3 observers. These data were then subjected to survival analysis with multivariate Cox regression using a composite endpoint of death, lung transplantation, a decline of≥10% in forced vital capacity (FVC), or hospitalization.

RESULTS

Of the 89 possible UIP patients included, 74 (83%) were reclassified as probable UIP and 15 (17%) as indeterminate for UIP. Probable UIP patients were more likely to meet the composite endpoint (56/74 [75.7%] vs. 5/15 [33%] patients; HR [IC 95%] =3.12 [1.24; 7.83], P=0.015). Multivariate analysis indicated that the probable UIP pattern was associated with significantly increased risk of reaching the composite endpoint (HR [95% CI]=2.85[1.00; 8.10], P=0.049).

CONCLUSION

The majority of possible UIP diagnoses corresponded to probable UIP, which was associated with a significantly worse prognosis than indeterminate for UIP. This distinction between these two CT patterns emphasizes the relevance of the new international guidelines for the diagnosis of IPF.

A retrospective study on the predictive implications of clinical characteristics and therapeutic management in patients with rheumatoid arthritis-associated interstitial lung disease

BACKGROUND

Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is associated with significant morbidity and is a critical cause of mortality in patients with RA.

OBJECTIVE

Our aim was to evaluate predictive and prognostic factors for RA-ILD and to describe the therapeutic management of the condition from a large China cohort.

METHODS

This was a retrospective cohort study. We collected data of 1121 RA patients who underwent chest HRCT from 2008 to 2017. Patients without ILD at RA diagnosis were included in the analysis. The development and evolution of ILD in RA patients were followed up. Determinants of ILD development and progression were identified through multivariable logistic analysis. Cox hazards analysis was used to determine significant variables associated with survival.

RESULTS

A total of 923 patients without ILD at RA diagnosis were identified and enrolled. Among them, 278 cases (30.12%) were diagnosed as ILD during follow-up. Logistic regression analysis showed that advanced age (> 60 years old) at RA onset (OR: 1.485), male (OR: 1.882), short duration of RA (0~5 years) (OR: 2.099), RF positive (OR: 1.728), elevated lactate dehydrogenase (LDH) (OR: 3.032), and no medication (OR: 1.833) were closely correlated to the development of RA-ILD. No correlation was found between ILD development and traditional DMARDs such as methotrexate and leflunomide. According to the follow-up data, 83 RA-ILD patients were identified as interstitial lung disease (ILD) progression, and 102 participants were stable. Logistic regression modeling demonstrated that DLCO% < 45% (OR: 3.025) and UIP possible pattern on HRCT (OR: 3.476) were independent risk factors for the ILD progression. No correlation was found between ILD progression and traditional DMARDs such as methotrexate and leflunomide. A total of 53 RA-ILD deaths occurred during follow-up. Cox hazards analysis revealed that advanced age (> 60 years old) at RA-ILD diagnosis (HR: 3.181) and extensive lung involvement on HRCT (HR: 2.401) were associated with worse survival. Treatment with cyclophosphamide (HR: 0.210) was associated with better survival.

CONCLUSIONS

Advanced age, male, short duration of RA, RF positive, elevated LDH, and no medication are closely correlated with RA-ILD. No correlation was found between traditional DMARDs and ILD development. DLCO% < 45% and UIP possible pattern are predictive factors for ILD progression. No correlation was found between traditional DMARDs and ILD progression. Advanced age and extensive lung involvement on HRCT independently predict mortality; cyclophosphamide treatment helps to improve the prognosis of RA-ILD.Key Points• We designed this study to investigate the predictive and prognostic factors for RA-ILD and to explore the potential role of DMARDs in the evolution of RA-ILD from the development to progression and death.• Patients without ILD at RA diagnosis were enrolled and followed up retrospectively.• Our results showed that no correlation was found between traditional DMARDs and the development and progression of ILD, and regular treatment may improve the development of RA-ILD.• Our results revealed that clinical variables appeared predictive implications for the diagnosis of ILD and physiological and radiological variables appeared predictive implications for the prognosis of ILD, which can provide reference to rheumatologists and help to improve poor prognosis of RA-ILD.

An Algorithmic Approach to the Interpretation of Diffuse Lung Disease on Chest CT Imaging: A Theory of Almost Everything

We propose an algorithmic approach to the interpretation of diffuse lung disease on high-resolution CT. Following an initial review of pertinent lung anatomy, the following steps are included. Step 1: a preliminary review of available chest radiographs, including the "scanogram" obtained at the time of the CT examination. Step 2: a review of optimal methods of data acquisition and reconstruction, emphasizing the need for contiguous high-resolution images throughout the entire thorax. Step 3: initial uninterrupted scrolling of contiguous high-resolution images throughout the chest to establish the quality of examination as well as an overview of the presence and extent of disease. Step 4: determination of one of three predominant categories - primarily reticular disease, nodular disease, or diseases associated with diffuse alteration in lung density. Based on this determination, one of the three following Steps are followed: Step 5: evaluation of cases primarily involving diffuse lung reticulation; Step 6: evaluation of cases primarily resulting in diffuse lung nodules; and Step 7: evaluation of cases with diffuse alterations in lung density including those with diffusely diminished lung density vs those with heterogenous or diffusely increased lung density, respectively. It is anticipated that this algorithmic approach will substantially enhance initial interpretations of a wide range of pulmonary disease.

New Developments in Imaging Idiopathic Pulmonary Fibrosis With Hyperpolarized Xenon Magnetic Resonance Imaging

Idiopathic pulmonary fibrosis (IPF) is a progressive pulmonary disease that is ultimately fatal. Although the diagnosis of IPF has been revolutionized by high-resolution computed tomography, this imaging modality still exhibits significant limitations, particularly in assessing disease progression and therapy response. The need for noninvasive regional assessment has become more acute in light of recently introduced novel therapies and numerous others in the pipeline. Thus, it will likely be valuable to complement 3-dimensional imaging of lung structure with 3-dimensional regional assessment of function. This challenge is well addressed by hyperpolarized (HP) Xe magnetic resonance imaging (MRI), exploiting the unique properties of this inert gas to image its distribution, not only in the airspaces, but also in the interstitial barrier tissues and red blood cells. This single-breath imaging exam could ultimately become the ideal, noninvasive tool to assess pulmonary gas-exchange impairment in IPF. This review article will detail the evolution of HP Xe MRI from its early development to its current state as a clinical research platform. It will detail the key imaging biomarkers that can be generated from the Xe MRI examination, as well as their potential in IPF for diagnosis, prognosis, and assessment of therapeutic response. We conclude by discussing the types of studies that must be performed for HP Xe MRI to be incorporated into the IPF clinical algorithm and begin to positively impact IPF disease diagnosis and management.

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.

Radiographic Differentiation of Advanced Fibrocystic Lung Diseases

The concept of end-stage lung disease suggests a final common pathway for most diffuse parenchymal lung diseases. In accordance with this concept, end-stage disease is characterized radiographically and pathologically by the presence of extensive honeycombing. However, sequential computed tomographic (CT) scans obtained from patients with chronic diffuse lung disease evolve over time to show various advanced lung disease patterns other than honeycombing. In addition, several radiographically distinct honeycomb patterns, including microcystic, macrocystic, mixed, and combined emphysema and honeycombing, differentiate one advanced lung disease from another. For example, usual interstitial pneumonia (IP) usually shows mixed microcystic and macrocystic honeycombing. In contrast, CT images of long-standing fibrotic nonspecific IP typically show only small, scattered foci of honeycombing; instead, most enlarged airspaces observed in the advanced stage of this disease represent dilatation of bronchioles. In desquamative IP and pulmonary Langerhans cell histiocytosis, focal opacities typically evolve into emphysema-like lesions seen on CT imaging. In combined pulmonary fibrosis and emphysema and sarcoidosis, the cysts tend to be larger than those observed in usual IP. Sequential CT scans in other chronic, diffuse lung diseases also show various distinctive changes. This article highlights radiographic patterns of lung destruction that belie a single common pathway to end-stage lung disease. Recognition of distinct radiographic patterns of lung destruction can help differentiate diffuse parenchymal lung diseases, even in advanced stages of disease evolution.

Longitudinal micro-CT as an outcome measure of interstitial lung disease in TNF-transgenic mice

INTRODUCTION

Rheumatoid arthritis associated interstitial lung disease (RA-ILD) is a debilitating condition with poor survival prognosis. High resolution computed tomography (CT) is a common clinical tool to diagnose RA-ILD, and is increasingly being adopted in pre-clinical studies. However, murine models recapitulating RA-ILD are lacking, and CT outcomes for inflammatory lung disease have yet to be formally validated. To address this, we validate μCT outcomes for ILD in the tumor necrosis factor transgenic (TNF-Tg) mouse model of RA.

METHODS

Cross sectional μCT was performed on cohorts of male TNF-Tg mice and their WT littermates at 3, 4, 5.5 and 12 months of age (n = 4-6). Lung μCT outcomes measures were determined by segmentation of the μCT datasets to generate Aerated and Tissue volumes. After each scan, lungs were obtained for histopathology and 3 sections stained with hematoxylin and eosin. Automated histomorphometry was performed to quantify the tissue area (nuclei, cytoplasm, and extracellular matrix) and aerated area (white space) within the tissue sections. Spearman's correlation coefficients were used to evaluate the extent of association between μCT imaging and histopathology endpoints.

RESULTS

TNF-Tg mice had significantly greater tissue volume, total lung volume and mean intensity at all timepoints compared to age matched WT littermates. Histomorphometry also demonstrated a significant increase in tissue area at 3, 4, and 5.5 months of age in TNF-Tg mice. Lung tissue volume was correlated with lung tissue area (ρ = 0.81, p<0.0001), and normalize lung aerated volume was correlated with normalized lung air area (ρ = 0.73, p<0.0001).

CONCLUSIONS

We have validated in vivo μCT as a quantitative biomarker of ILD in mice. Further, development of longitudinal measures is critical for dissecting pathologic progression of ILD, and μCT is a useful non-invasive method to study lung inflammation in the TNF-Tg mouse model.

Pathology and radiology correlation of idiopathic interstitial pneumonias

By nature, idiopathic interstitial pneumonias have been diagnosed in a multidisciplinary manner. As classifications have been subject to significant refinement over the last decade, the importance of correlating clinical, radiologic, and pathologic information to arrive at a diagnosis, which will predict prognosis in any given patient, has become increasingly recognized. In 2013, the American Thoracic Society and European Respiratory Society updated the idiopathic interstitial pneumonias classification scheme, addressing the most recent updates in the field. The purpose of this review is to highlight the correlations between radiologic and pathologic findings in idiopathic interstitial pneumonias while using updated classification schemes and naming conventions.

Imaging of Idiopathic Pulmonary Fibrosis

Idiopathic interstitial pneumonias are a heterogeneous group of diffuse lung diseases characterized by distinct clinicopathologic entities with the usual interstitial pneumonia (UIP) being the most common. The pattern of UIP can be seen in idiopathic pulmonary fibrosis (IPF) as well as in secondary causes, most commonly in connective tissue diseases. IPF is usually progressive and associated with a very poor prognosis, and newer therapies pose a risk of serious complications; therefore, diagnostic certainty is crucial. This article reviews the radiologic findings in UIP with clinical correlation and histopathologic features along with its significance for prognosis and patients monitoring.