Quantitative high-resolution computed tomography fibrosis score: performance characteristics in idiopathic pulmonary fibrosis

Interstitial Lung Disease Associated with Systemic Sclerosis

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by a tripod combining vasculopathy, fibrosis, and immune-mediated inflammatory processes. The prevalence of interstitial lung disease (ILD) in SSc varies according to the methods used to detect it, ranging from 25 to 95%. The fibrotic and vascular pulmonary manifestations of SSc, particularly ILD, are the main causes of morbidity and mortality, contributing to 35% of deaths. Although early trials were conducted with cyclophosphamide, more recent randomized controlled trials have been performed to assess the efficacy and tolerability of several medications, mostly mycophenolate, rituximab, tocilizumab, and nintedanib. Although many uncertainties remain, expert consensus is emerging to optimize the therapeutic management and to provide clinicians with evidence-based clinical practice guidelines for patients with SSc-ILD. This article provides an overview, in the light of the latest advances, of the available evidence for the diagnosis and management of SSc-ILD.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review

Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

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.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern

Rationale: Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. Objectives: We hypothesized that fibrosis extent provides information beyond visually assessed CT pattern that is useful for outcome prediction. Methods: We performed a retrospective analysis of chest CT, demographics, longitudinal pulmonary function, and transplantation-free survival among participants in the Pulmonary Fibrosis Foundation Patient Registry. CT pattern was classified visually according to the 2018 usual interstitial pneumonia criteria. Extent of fibrosis was objectively quantified using data-driven textural analysis. We used Kaplan-Meier plots and Cox proportional hazards and linear mixed-effects models to evaluate the relationships between CT-derived metrics and outcomes. Results: Visual assessment and quantitative analysis were performed on 979 enrollment CT scans. Linear mixed-effect modeling showed that greater baseline fibrosis extent was significantly associated with the annual rate of decline in forced vital capacity. In multivariable models that included CT pattern and fibrosis extent, quantitative fibrosis extent was strongly associated with transplantation-free survival independent of CT pattern (hazard ratio, 1.04; 95% confidence interval, 1.04-1.05; P < 0.001; C statistic = 0.73). Conclusions: The extent of lung fibrosis by quantitative CT is a strong predictor of physiologic progression and survival, independent of visually assessed CT pattern.

Imaging of pulmonary fibrosis in children: A review, with proposed diagnostic criteria

Computed tomography (CT) imaging findings of pulmonary fibrosis are well established for adults and have been shown to correlate with prognosis and outcome. Recognition of fibrotic CT findings in children is more limited. With approved treatments for adult pulmonary fibrosis, it has become critical to define CT criteria for fibrosis in children, to identify patients in need of treatment and those eligible for clinical trials. Understanding how pediatric fibrosis compares with idiopathic pulmonary fibrosis and other causes of fibrosis in adults is increasingly important as these patients transition to adult care teams. Here, we review what is known regarding the features of pulmonary fibrosis in children compared with adults. Pulmonary fibrosis in children may be associated with genetic surfactant dysfunction disorders, autoimmune systemic disorders, and complications after radiation, chemotherapy, transplantation, and other exposures. Rather than a basal-predominant usual interstitial pneumonia pattern with honeycombing, pediatric fibrosis is primarily characterized by reticulation, traction bronchiectasis, architectural distortion, or cystic lucencies/abnormalities. Ground-glass opacities are more frequent in children with fibrotic interstitial lung disease than adults, and disease distribution appears more diffuse, without clearly defined axial or craniocaudal predominance. Following discussion and consensus amongst a panel of expert radiologists, pathologists and physicians, distinctive disease features were integrated to develop criteria for the first global Phase III trial in children with pulmonary fibrosis.

Rationale and design of the prognostic transcriptomic signature in fibrotic hypersensitivity pneumonitis (PREDICT) study

Hypersensitivity pneumonitis is an immunologically mediated form of lung disease, resulting from inhalational exposure to a large variety of antigens. A subgroup of patients with fibrotic hypersensitivity pneumonitis (FHP) develop symptomatic, functional and radiographic disease progression. Mortality occurs primarily from respiratory failure as a result of progressive and self-sustaining lung injury that often occurs despite immunosuppression and removal of the inciting antigen. The development and validation of a prognostic transcriptomic signature for FHP (PREDICT-HP) is an observational multicentre cohort study designed to explore a transcriptomic signature from peripheral blood mononuclear cells in patients with FHP that is predictive of disease progression. This article describes the design and rationale of the PREDICT-HP study. This study will enrol ∼135 patients with FHP at approximately seven academic medical sites. Participants with a confirmed diagnosis of FHP are followed over 24 months and undergo physical examinations, self-administered questionnaires, chest computed tomography, pulmonary function tests, a 6-min walk test and blood testing for transcriptomic analyses. At each 6-month follow-up visit the study will assess the participants' clinical course and clinical events including hospitalisations and respiratory exacerbations. The PREDICT study has the potential to enhance our ability to predict disease progression and fundamentally advance our understanding of the pathobiology of FHP disease progression.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs

OBJECTIVES

To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

METHODS

To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM.

RESULTS

DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01).

CONCLUSIONS

A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC.

CLINICAL RELEVANCE STATEMENT

Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.

KEY POINTS

• A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

BACKGROUND

Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis.

METHODS

CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Correlation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progression rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test.

RESULTS

The follow-up was 3.8 ± 1.5 years encompassing 105 CT scans, with 3.3 ± 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, ρ = -0.30 [95% CI: -0.16 to -0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplantations. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR ≥ 4%/year during the first two years had a poorer prognosis (p = 0.001).

CONCLUSIONS

Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome.

The potential role of artificial intelligence in the clinical practice of interstitial lung disease

Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.

Pirfenidone in fibrotic hypersensitivity pneumonitis: a double-blind, randomised clinical trial of efficacy and safety

BACKGROUND

Fibrotic hypersensitivity pneumonitis (FHP) is an irreversible lung disease with high morbidity and mortality. We sought to evaluate the safety and effect of pirfenidone on disease progression in such patients.

METHODS

We conducted a single-centre, randomised, double-blinded, placebo-controlled trial in adults with FHP and disease progression. Patients were assigned in a 2:1 ratio to receive either oral pirfenidone (2403 mg/day) or placebo for 52 weeks. The primary end point was the mean absolute change in the per cent predicted forced vital capacity (FVC%). Secondary end points included progression-free survival (PFS, time to a relative decline ≥10% in FVC and/or diffusing capacity of the lung for carbon monoxide (DLCO), acute respiratory exacerbation, a decrease of ≥50 m in the 6 min walk distance, increase or introduction of immunosuppressive drugs or death), change in FVC slope and mean DLCO%, hospitalisations, radiological progression of lung fibrosis and safety.

RESULTS

After randomising 40 patients, enrolment was interrupted by the COVID-19 pandemic. There was no significant between-group difference in FVC% at week 52 (mean difference -0.76%, 95% CI -6.34 to 4.82). Pirfenidone resulted in a lower rate of decline in the adjusted FVC% at week 26 and improved PFS (HR 0.26, 95% CI 0.12 to 0.60). Results for other secondary end points showed no significant difference between groups. No deaths occurred in the pirfenidone group and one death (respiratory) occurred in the placebo group. There were no treatment-emergent serious adverse events.

CONCLUSIONS

The trial was underpowered to detect a difference in the primary end point. Pirfenidone was found to be safe and improved PFS in patients with FHP.

TRIAL REGISTRATION MUMBER

NCT02958917.

Deep Learning Assessment of Progression of Emphysema and Fibrotic Interstitial Lung Abnormality

Rationale: Although studies have evaluated emphysema and fibrotic interstitial lung abnormality individually, less is known about their combined progression. Objectives: To define clinically meaningful progression of fibrotic interstitial lung abnormality in smokers without interstitial lung disease and evaluate the effects of fibrosis and emphysema progression on mortality. Methods: Emphysema and pulmonary fibrosis were assessed on the basis of baseline and 5-year follow-up computed tomography scans of 4,450 smokers in the COPDGene Study using deep learning algorithms. Emphysema was classified as absent, trace, mild, moderate, confluent, or advanced destructive. Fibrosis was expressed as a percentage of lung volume. Emphysema progression was defined as an increase by at least one grade. A hybrid distribution and anchor-based method was used to determine the minimal clinically important difference in fibrosis. The relationship between progression and mortality was evaluated using multivariable shared frailty models using an age timescale. Measurements and Main Results: The minimal clinically important difference for fibrosis was 0.58%. On the basis of this threshold, 2,822 (63%) had progression of neither emphysema nor fibrosis, 841 (19%) had emphysema progression alone, 512 (12%) had fibrosis progression alone, and 275 (6.2%) had progression of both. Compared with nonprogressors, hazard ratios for mortality were 1.42 (95% confidence interval, 1.11-1.82) in emphysema progressors, 1.49 (1.14-1.94) in fibrosis progressors, and 2.18 (1.58-3.02) in those with progression of both emphysema and fibrosis. Conclusions: In smokers without known interstitial lung disease, small changes in fibrosis may be clinically significant, and combined progression of emphysema and fibrosis is associated with increased mortality.

Defining and Promoting Pediatric Pulmonary Health: Assessing Lung Function and Structure

Lifelong respiratory health is rooted in the structural and functional development of the respiratory system in early life. Exposures and interventions antenatally through childhood can influence lung development into young adulthood, the life stage with the highest achievable lung function. Because early respiratory health sets the stage for adult lung function trajectories and risk of developing chronic obstructive pulmonary disease, understanding how to promote lung health in children will have far reaching personal and population benefits. To achieve this, it is critical to have accurate and precise measures of structural and functional lung development that track throughout life stages. From this foundation, evaluation of environmental, genetic, metabolic, and immune mechanisms involved in healthy lung development can be investigated. These goals require the involvement of general pediatricians, pediatric subspecialists, patients, and researchers to design and implement studies that are broadly generalizable and applicable to otherwise healthy and chronic disease populations. This National Institutes of Health workshop report details the key gaps and opportunities regarding lung function and structure.

In vivo polarisation sensitive optical coherence tomography for fibrosis assessment in interstitial lung disease: a prospective, exploratory, observational study

INTRODUCTION

Endobronchial polarisation sensitive optical coherence tomography (EB-PS-OCT) is a bronchoscopic imaging technique exceeding resolution of high-resolution CT (HRCT) by 50-fold. It detects collagen birefringence, enabling identification and quantification of fibrosis.

STUDY AIM

To assess pulmonary fibrosis in interstitial lung diseases (ILD) patients with in vivo EB-PS-OCT using histology as reference standard.

PRIMARY OBJECTIVE

Visualisation and quantification of pulmonary fibrosis by EB-PS-OCT.

SECONDARY OBJECTIVES

Comparison of EB-PS-OCT and HRCT detected fibrosis with histology, identification of ILD histological features in EB-PS-OCT images and comparison of ex vivo PS-OCT results with histology.

METHODS

Observational prospective exploratory study. Patients with ILD scheduled for transbronchial cryobiopsy or surgical lung biopsy underwent in vivo EB-PS-OCT imaging prior to tissue acquisition. Asthma patients were included as non-fibrotic controls. Per imaged lung segment, fibrosis was automatically quantified assessing the birefringent area in EB-PS-OCT images. Fibrotic extent in corresponding HRCT areas and biopsies were compared with EB-PS-OCT detected fibrosis. Microscopic ILD features were identified on EB-PS-OCT images and matched with biopsies from the same segment.

RESULTS

19 patients were included (16 ILD; 3 asthma). In 49 in vivo imaged airway segments the parenchymal birefringent area was successfully quantified and ranged from 2.54% (no to minimal fibrosis) to 21.01% (extensive fibrosis). Increased EB-PS-OCT detected birefringent area corresponded to increased histologically confirmed fibrosis, with better predictive value than HRCT. Microscopic ILD features were identified on both in vivo and ex vivo PS-OCT images.

CONCLUSIONS

EB-PS-OCT enables pulmonary fibrosis quantification, thereby has potential to serve as an add-on bronchoscopic imaging technique to diagnose and detect (early) fibrosis in ILD.

Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges

The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.

Serum antibodies to peptidylarginine deiminase-4 in rheumatoid arthritis associated-interstitial lung disease are associated with decreased lung fibrosis and improved survival

OBJECTIVE

Interstitial lung disease (ILD) is a leading cause of mortality in rheumatoid arthritis (RA), particularly in those with the usual interstitial pneumonia subtype (RA-UIP). Serum antibodies to peptidylarginine deiminase type 4 (anti-PAD4), particularly a subset that cross-react with PAD3 (PAD3/4XR), have been associated with imaging evidence of ILD. We aimed to determine the specificity of anti-PAD4 antibodies in RA-ILD and to examine associations with markers of ILD severity.

METHODS

48 RA-ILD and 31 idiopathic pulmonary fibrosis (IPF) patients were identified from the National Jewish Health Biobank. RA-ILD subtype was defined by imaging pattern on high-resolution chest computed tomography (CT), and serum was tested for anti-PAD4 and anti-PAD3/4XR antibodies. Antibody prevalence, measures of ILD severity (% predicted forced vital capacity, FVC; % predicted diffusion capacity carbon monoxide, DLCO; quantitative CT fibrosis) and mortality were compared between groups.

RESULTS

Anti-PAD4 antibodies were present in 9/48 (19%) subjects with RA-ILD and no subjects with IPF. Within RA-ILD, anti-PAD4 antibodies were found almost exclusively in RA-UIP (89%). Within RA-UIP subjects, % predicted FVC was higher in anti-PAD4+ subjects, and this finding was most strongly associated with anti-PAD3/4XR antibodies. In addition, quantitative CT fibrosis score was lower in anti-PAD4+ RA-UIP subjects, including those with mono-reactive anti-PAD4 antibodies and anti-PAD3/4XR antibodies. Anti-PAD4+ RA-UIP subjects also exhibited decreased mortality.

CONCLUSIONS

We demonstrate the presence of serum anti-PAD4 antibodies in a subset of patients with RA-UIP that were notably associated with better lung function, less fibrosis and decreased mortality.

Incidence of Interstitial Lung Abnormalities: The MESA Lung Study

The incidence of newly developed interstitial lung abnormalities (ILA) and fibrotic ILA have not been previously reported.Trained thoracic radiologists evaluated 13 944 cardiac CT scans for the presence of ILA in 6197 Multi-Ethnic Study of Atherosclerosis longitudinal cohort study participants >45 years of age from 2000 to 2012. 5% of the scans were re-read by the same or a different observer in a blinded fashion. After exclusion of participants with ILA at baseline, incidence rates and incidence rate ratios for ILA and fibrotic ILA were calculated.The intra-reader agreement of ILA was 92.0% (Gwet AC1=0.912, ICC=0.982) and the inter-reader agreement of ILA was 83.5% (Gwet AC1=0.814; ICC=0.969). Incidence of ILA and fibrotic ILA was estimated to be 13.1 cases/1000 person-years and 3.5/1000 person-years, respectively. In multivariable analyses, age (HR 1.06 (1.05, 1.08), p <0.001; HR 1.08 (1.06, 1.11), p <0.001), high attenuation area (HAA) at baseline (HR 1.05 (1.03, 1.07), p <0.001; HR 1.06 (1.02, 1.10), p=0.002), and the MUC5B promoter SNP (HR 1.73 (1.17, 2.56) p=0.01; HR 4.96 (2.68, 9.15), p <0.001) were associated with incident ILA and fibrotic ILA, respectively. Ever smoking (HR 2.31 (1.34, 3.96), p= 0.002) and an IPF polygenic risk score (HR 2.09 (1.61-2.71), p<0.001) were associated only with incident fibrotic ILA.Incident ILA and fibrotic ILA were estimated by review of cardiac imaging studies. These findings may lead to wider application of a screening tool for atherosclerosis to identify preclinical lung disease.

Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra

Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm^-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.

Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis

OBJECTIVE

To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF).

METHODS

Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (D_LCO) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for D_LCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years.

RESULTS

In total, 161 patients (median age [IQR], 68 [62-73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson's r, 0.40 for CT-Norm% and - 0.37 for CT-Fib%; both p < 0.001) and D_LCO (0.52 for CT-Norm% and - 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and D_LCO (HRs, 0.98 [95% CI 0.97-0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01-1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for D_LCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78-0.85] vs. 0.82 [0.79-0.86]; p = 0.75).

CONCLUSION

CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF.

KEY POINTS

• Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis. • CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson's r, 0.40 for normal and - 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson's r, 0.52 and - 0.46, respectively). • Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables.

Incidence and Progression of Fibrotic Lung Disease in an At-Risk Cohort

Rationale: Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis and develop preclinical pulmonary fibrosis (PrePF). Objectives: We defined the incidence and progression of new-onset PrePF and its relationship to survival among first-degree relatives of families with FIP. Methods: This is a cohort study of family members with FIP who were initially screened with a health questionnaire and chest high-resolution computed tomography (HRCT) scan, and approximately 4 years later, the evaluation was repeated. A total of 493 asymptomatic first-degree relatives of patients with FIP were evaluated at baseline, and 296 (60%) of the original subjects participated in the subsequent evaluation. Measurements and Main Results: The median interval between HRCTs was 3.9 years (interquartile range, 3.5-4.4 yr). A total of 252 subjects who agreed to repeat evaluation were originally determined not to have PrePF at baseline; 16 developed PrePF. A conservative estimate of the annual incidence of PrePF is 1,023 per 100,000 person-years (95% confidence interval, 511-1,831 per 100,000 person-years). Of 44 subjects with PrePF at baseline, 38.4% subjects had worsening dyspnea compared with 15.4% of those without PrePF (P = 0.002). Usual interstitial pneumonia by HRCT (P < 0.0002) and baseline quantitative fibrosis score (P < 0.001) are also associated with worsening dyspnea. PrePF at the initial screen is associated with decreased survival (P < 0.001). Conclusions: The incidence of PrePF in this at-risk population is at least 100-fold higher than that reported for sporadic idiopathic pulmonary fibrosis (IPF). Although PrePF and IPF represent distinct entities, our study demonstrates that PrePF, like IPF, is progressive and associated with decreased survival.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Culture independent markers of nontuberculous mycobacterial (NTM) lung infection and disease in the cystic fibrosis airway

Nontuberculous mycobacteria (NTM) are opportunistic pathogens that affect a relatively small but significant portion of the people with cystic fibrosis (CF), and may cause increased morbidity and mortality in this population. Cultures from the airway are the only test currently in clinical use for detecting NTM. Culture techniques used in clinical laboratories are insensitive and poorly suited for population screening or to follow progression of disease or treatment response. The lack of sensitive and quantitative markers of NTM in the airway impedes patient care and clinical trial design, and has limited our understanding of patterns of acquisition, latency and pathogenesis of disease. Culture-independent markers of NTM infection have the potential to overcome many of the limitations of standard NTM cultures, especially the very slow growth, inability to quantitate bacterial burden, and low sensitivity due to required decontamination procedures. A range of markers have been identified in sputum, saliva, breath, blood, urine, as well as radiographic studies. Proposed markers to detect presence of NTM or transition to NTM disease include bacterial cell wall products and DNA, as well as markers of host immune response such as immunoglobulins and the gene expression of circulating leukocytes. In all cases the sensitivity of culture-independent markers is greater than standard cultures; however, most do not discriminate between various NTM species. Thus, each marker may be best suited for a specific clinical application, or combined with other markers and traditional cultures to improve diagnosis and monitoring of treatment response.

Associations of hiatus hernia with CT-based interstitial lung changes: the MESA Lung Study

BACKGROUND

Hiatus hernia (HH) is prevalent in adults with pulmonary fibrosis. We hypothesised that HH would be associated with markers of lung inflammation and fibrosis among community-dwelling adults and stronger among MUC5B (rs35705950) risk allele carriers.

METHODS

In the Multi-Ethnic Study of Atherosclerosis, HH was assessed from cardiac and full-lung computed tomography (CT) scans performed at Exam 1 (2000-2002, n=3342) and Exam 5 (2010-2012, n=3091), respectively. Percentage of high attenuation areas (HAAs; percentage of voxels with attenuation between -600 and -250 HU) was measured from cardiac and lung scans. Interstitial lung abnormalities (ILAs) were examined from Exam 5 scans (n=2380). Regression models were used to examine the associations of HH with HAAs, ILAs and serum matrix metalloproteinase-7 (MMP-7), and adjusted for age, sex, race/ethnicity, educational attainment, smoking, height, weight and scanner parameters for HAA analysis.

RESULTS

HH detected from Exam 5 scans was associated with a mean percentage difference in HAAs of 2.23% (95% CI 0.57-3.93%) and an increase of 0.48% (95% CI 0.07-0.89%) per year, particularly in MUC5B risk allele carriers (p-value for interaction=0.02). HH was associated with ILAs among those <80 years of age (OR for ILAs 1.78, 95% CI 1.14-2.80) and higher serum MMP-7 level among smokers (p-value for smoking interaction=0.04).

CONCLUSIONS

HH was associated with more HAAs over time, particularly among MUC5B risk allele carriers, and ILAs in younger adults, and may be a risk factor in the early stages of interstitial lung disease.

Physical activity in idiopathic pulmonary fibrosis: Longitudinal change and minimal clinically important difference

BACKGROUND AND OBJECTIVE

Reference values of physical activity to interpret longitudinal changes are not available in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to define the minimal clinical important difference (MCID) of longitudinal changes in physical activity in patients with IPF.

METHODS

Using accelerometry, physical activity (steps per day) was measured and compared at baseline and 6-months follow-up in patients with IPF. We calculated MCID of daily step count using multiple anchor-based and distribution-based methods. Forced vital capacity and 6-minute walk distance were applied as anchors in anchor-based methods. Effect size and standard error of measurement were used to calculate MCID in distribution-based methods.

RESULTS

One-hundred and five patients were enrolled in the study (mean age: 68.5 ± 7.5 years). Step count significantly decreased from baseline to 6-months follow-up (-461 ± 2402, p = .031). MCID calculated by anchor-based and distribution-based methods ranged from 570-1358 steps.

CONCLUSION

Daily step count significantly declined over 6-months in patients with IPF. MCID calculated by multiple anchor-based and distribution-based methods was 570 to 1358 steps/day. These findings contribute to interpretation of the longitudinal changes of physical activity that will assist its use as a clinical and research outcome in patients with IPF.

Machine learning in radiology: the new frontier in interstitial lung diseases

Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.

METHODS

This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes.

RESULTS

CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression-free survival (HR 1.14, p < 0.0001).

CONCLUSION

In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.

Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-resolution Computed Tomography

RATIONALE Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. OBJECTIVES To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA), trained and validated in the identification of UIP-like features on HRCT (UIP probability), in a large cohort of well characterised patients with progressive fibrotic lung disease, drawn from a national registry. METHODS SOFIA and radiologist-UIP probabilities were converted to PIOPED-based UIP probability categories (UIP not included in the differential: 0-4%, low probability of UIP: 5-29%, intermediate probability of UIP: 30-69%, high probability of UIP: 70-94%, and pathognomonic for UIP:95-100%) and their prognostic utility assessed using Cox proportional hazards modelling. MEASUREMENTS AND MAIN RESULTS On multivariable analysis adjusting for age, gender, guideline based radiologic diagnosis and disease severity (using total ILD extent on HRCT, %predicted FVC, DLco or the CPI), only SOFIA-UIP probability PIOPED categories predicted survival. SOFIA-PIOPED UIP probability categories remained prognostically significant in patients considered indeterminate (n=83) by expert radiologist consensus (HR1.73, p<0.0001, 95%CI 1.40-2.14). In patients undergoing surgical lung biopsy (SLB) (n=86), after adjusting for guideline-based histologic pattern and total ILD extent on HRCT, only SOFIA-PIOPED probabilities were predictive of mortality (HR1.75, p<0.0001, 95%CI 1.37-2.25). CONCLUSIONS Deep learning-based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared to expert radiologist evaluation or guideline-based histologic pattern. In principle this tool may be useful in multidisciplinary characterisation of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.

Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline

Background: This American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana de Tórax guideline updates prior idiopathic pulmonary fibrosis (IPF) guidelines and addresses the progression of pulmonary fibrosis in patients with interstitial lung diseases (ILDs) other than IPF. Methods: A committee was composed of multidisciplinary experts in ILD, methodologists, and patient representatives. 1) Update of IPF: Radiological and histopathological criteria for IPF were updated by consensus. Questions about transbronchial lung cryobiopsy, genomic classifier testing, antacid medication, and antireflux surgery were informed by systematic reviews and answered with evidence-based recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. 2) Progressive pulmonary fibrosis (PPF): PPF was defined, and then radiological and physiological criteria for PPF were determined by consensus. Questions about pirfenidone and nintedanib were informed by systematic reviews and answered with evidence-based recommendations using the GRADE approach. Results:1) Update of IPF: A conditional recommendation was made to regard transbronchial lung cryobiopsy as an acceptable alternative to surgical lung biopsy in centers with appropriate expertise. No recommendation was made for or against genomic classifier testing. Conditional recommendations were made against antacid medication and antireflux surgery for the treatment of IPF. 2) PPF: PPF was defined as at least two of three criteria (worsening symptoms, radiological progression, and physiological progression) occurring within the past year with no alternative explanation in a patient with an ILD other than IPF. A conditional recommendation was made for nintedanib, and additional research into pirfenidone was recommended. Conclusions: The conditional recommendations in this guideline are intended to provide the basis for rational, informed decisions by clinicians.

Radiologic evaluation of compensatory lung growth using computed tomography by comparison with histological data from a large animal model

Non-invasive analysis using computed tomography (CT) data may be a promising candidate to evaluate neo-alveolarization in adult lungs following lung resection. This study evaluates and compares the validity of CT analysis with histologic morphometry for compensatory lung growth in a large animal model. We calculated the radiologic tissue volume and the radiologic lung weight from CT data taken at 1, 3, and 6 months post-surgery on 15 male beagle dogs that had a right thoractotomy, bilobectomy, or pneumonectomy (n = 5 in each group). Results were analyzed using one-way ANOVA and were subsequently compared to histologic findings of tissue samples at 6 months post-surgery using Pearson's correlation. An increase in radiologic tissue volume and radiologic lung weight was identified, which was positively correlated with histologic lung parenchymal amounts (correlation coefficient = 0.955 and 0.934, respectively, p < 0.001). Histology of lung specimens at 6 months post-surgery revealed an increase in the tissue amount in both Bilobectomy and Peumonectomy groups, which was consistent with compensatory lung growth. Radiologic tissue volume and radiologic lung weight reflected compensatory lung growth following lung resection. Radiologic assessment using CT data can be a promising clinical modality to evaluate postoperative lung growth.

CC-90001, a c-Jun N-terminal kinase (JNK) inhibitor, in patients with pulmonary fibrosis: design of a phase 2, randomised, placebo-controlled trial

Introduction

Idiopathic pulmonary fibrosis (IPF) is a progressive and often fatal interstitial lung disease (ILD); other ILDs have a progressive, fibrotic phenotype (PF-ILD). Antifibrotic agents can slow but not stop disease progression in patients with IPF or PF-ILD. c-Jun N-terminal kinases (JNKs) are stress-activated protein kinases implicated in the underlying mechanisms of fibrosis, including epithelial cell death, inflammation and polarisation of profibrotic macrophages, fibroblast activation and collagen production. CC-90001, an orally administered (PO), one time per day, JNK inhibitor, is being evaluated in IPF and PF-ILD.

Methods and analysis

This is a phase 2, randomised, double-blind, placebo-controlled study evaluating efficacy and safety of CC-90001 in patients with IPF (main study) and patients with PF-ILD (substudy). Both include an 8-week screening period, a 24-week treatment period, up to an 80-week active-treatment extension and a 4-week post-treatment follow-up. Patients with IPF (n=165) will be randomised 1:1:1 to receive 200 mg or 400 mg CC-90001 or placebo administered PO one time per day; up to 25 patients/arm will be permitted concomitant pirfenidone use. Forty-five patients in the PF-ILD substudy will be randomised 2:1 to receive 400 mg CC-90001 or placebo. The primary endpoint is change in per cent predicted forced vital capacity from baseline to Week 24 in patients with IPF.

Ethics and dissemination

This study will be conducted in accordance with Good Clinical Practice guidelines, Declaration of Helsinki principles and local ethical and legal requirements. Results will be reported in a peer-reviewed publication.

Trial registration number

NCT03142191.

CT of Post-Acute Lung Complications of COVID-19

The acute course of COVID-19 is variable and ranges from asymptomatic infection to fulminant respiratory failure. Patients recovering from COVID-19 can have persistent symptoms and CT abnormalities of variable severity. At 3 months after acute infection, a subset of patients will have CT abnormalities that include ground-glass opacity (GGO) and subpleural bands with concomitant pulmonary function abnormalities. At 6 months after acute infection, some patients have persistent CT changes to include the resolution of GGOs seen in the early recovery phase and the persistence or development of changes suggestive of fibrosis, such as reticulation with or without parenchymal distortion. The etiology of lung disease after COVID-19 may be a sequela of prolonged mechanical ventilation, COVID-19-induced acute respiratory distress syndrome (ARDS), or direct injury from the virus. Predictors of lung disease after COVID-19 include need for intensive care unit admission, mechanical ventilation, higher inflammatory markers, longer hospital stay, and a diagnosis of ARDS. Treatments of lung disease after COVID-19 are being investigated, including the potential of antifibrotic agents for prevention of lung fibrosis after COVID-19. Future research is needed to determine the long-term persistence of lung disease after COVID-19, its impact on patients, and methods to either prevent or treat it. © RSNA, 2021.

ACR Appropriateness Criteria® Diffuse Lung Disease

Diffuse lung disease, frequently referred to as interstitial lung disease, encompasses numerous disorders affecting the lung parenchyma. The potential etiologies of diffuse lung disease are broad with several hundred established clinical syndromes and pathologies currently identified. Imaging plays a critical role in diagnosis and follow-up of many of these diseases, although multidisciplinary discussion is the current standard for diagnosis of several DLDs. This document aims to establish guidelines for evaluation of diffuse lung diseases for 1) initial imaging of suspected diffuse lung disease, 2) initial imaging of suspected acute exacerbation or acute deterioration in cases of confirmed diffuse lung disease, and 3) clinically indicated routine follow-up of confirmed diffuse lung disease without acute deterioration. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Ground glass and fibrotic change in children with surfactant protein C dysfunction mutations

INTRODUCTION

Therapeutics exist to treat fibrotic lung disease in adults, but these have not been investigated in children. Defining biomarkers for pediatric fibrotic lung disease in children is crucial for clinical trials. Children with surfactant protein C (SFTPC) dysfunction mutations develop fibrotic lung disease over time. We evaluated chest computed tomography (CT) changes over time in children with SFTPC dysfunction mutations.

METHODS

We performed an institutional review board-approved retrospective review of children with SFTPC dysfunction mutations. We collected demographic and clinical information. Chest CT scans were evaluated using visual and computerized scores. Chest CT scores and pulmonary function tests were reviewed.

RESULTS

Eleven children were included. All children presented in infancy and four children suffered from respiratory failure requiring mechanical ventilation. Those who performed pulmonary function tests had stable forced vital capacities over time by percent predicted, but increased forced vital capacity in liters. CT findings evolved over time in most patients with earlier CT scans demonstrating ground glass opacities and later CT scans with more fibrotic features. In a pilot analysis, data-driven textural analysis software identified fibrotic features in children with SFTPC dysfunction that increased over time and correlated with visual CT scores.

DISCUSSION

We describe 11 children with SFTPC dysfunction mutations. Increases in forced vital capacity over time suggest that these children experience lung growth and that therapeutic intervention may maximize lung growth. Ground glass opacities are the primary early imaging findings while fibrotic features dominate later. CT findings suggest the development of and increases in fibrotic features that may serve as potential biomarkers for antifibrotic therapeutic trials.

A contemporary practical approach to the multidisciplinary management of unclassifiable interstitial lung disease

Fibrotic interstitial lung diseases (ILDs) frequently have nonspecific and overlapping clinical and radiological features, resulting in approximately 10-20% of patients with ILD lacking a clear diagnosis and thus being labelled with unclassifiable ILD. The objective of this review is to describe how patients with unclassifiable ILD should be evaluated and what impact specific clinical, radiological, and histopathological features may have on management decisions, focusing on patients with a predominantly fibrotic phenotype. We highlight recent data that have suggested an increasing role for antifibrotic medications in a variety of fibrotic ILDs, but justify the ongoing importance of making an accurate ILD diagnosis given the benefit of immunomodulatory therapies in many patient populations. We provide a practical approach to support management decisions that can be used by clinicians and tested by clinical researchers, and further identify the need for additional research to support a rational and standardised approach to the management of patients with unclassifiable ILD.

Persistent, Progressive Pulmonary Fibrosis and Epithelial Remodeling in Mice

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic interstitial lung disease with underlying mechanisms that have been primarily investigated in mice after intratracheal instillation of a single dose of bleomycin. However, the model has significant limitations, including transient fibrosis that spontaneously resolves and its failure to fully recapitulate the epithelial remodeling in the lungs of patients with IPF. Thus, there remains an unmet need for a preclinical model with features that more closely resemble the human disease. Repetitive intratracheal instillation of bleomycin has previously been shown to recapitulate some of these features, but the instillation procedure is complex, and the long-term consequences on epithelial remodeling and fibrosis persistence and progression remain poorly understood. Here, we developed a simplified repetitive bleomycin instillation strategy consisting of three bi-weekly instillations that leads to persistent and progressive pulmonary fibrosis. Lung histology demonstrates increased collagen deposition, fibroblast accumulation, loss of type I and type II alveolar epithelial cells within fibrotic areas, bronchiolization of the lung parenchyma with CCSP+ cells, remodeling of the distal lung into cysts reminiscent of simple honeycombing, and accumulation of hyperplastic transitional KRT8+ epithelial cells. Micro-computed tomographic imaging demonstrated significant traction bronchiectasis and subpleural fibrosis. Thus, the simplified repetitive bleomycin instillation strategy leads to progressive fibrosis and recapitulates the histological and radiographic characteristics of IPF. Compared with the single bleomycin instillation model, we suggest that the simplified repetitive instillation model may be better suited to address mechanistic questions about IPF pathogenesis and preclinical studies of antifibrotic drug candidates.

The evolution of computer-based analysis of high-resolution CT of the chest in patients with IPF

In patients with idiopathic pulmonary fibrosis (IPF), there is an urgent need of biomarkers which can predict disease behaviour or response to treatment. Most published studies report results based on continuous data which can be difficult to apply to individual patients in clinical practice. Having antifibrotic therapies makes it even more important that we can accurately diagnose and prognosticate in IPF patients. Advances in computer technology over the past decade have provided computer-based methods for objectively quantifying fibrotic lung disease on high-resolution CT of the chest with greater strength than visual CT analysis scores. These computer-based methods and, more recently, the arrival of deep learning-based image analysis might provide a response to these unsolved problems. The purpose of this commentary is to provide insights into the problems associated with visual interpretation of HRCT, describe of the current technologies used to provide quantification of disease on HRCT and prognostication in IPF patients, discuss challenges to the implementation of this technology and future directions.

Computer-Aided quantitative analysis in interstitial lung diseases - A pictorial review using CALIPER

Computer-based quantitative computed tomography analysis has a growing role in the clinical evaluation, prognosis, and longitudinal management of diffuse parenchymal diseases. It provides improved characterization and quantification of disease. The pulmonary vessel-related structure score is a purely computer-based parameter that cannot be evaluated by the human eye and allows us to prognosticate outcomes in patients with fibrosing interstitial lung disease.

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

Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To 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 Methods Patients 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. Results A 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). Conclusion Elastic 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. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

From Mouse to Man and Back: Closing the Correlation Gap between Imaging and Histopathology for Lung Diseases

Lung diseases such as fibrosis, asthma, cystic fibrosis, infection and cancer are life-threatening conditions that slowly deteriorate quality of life and for which our diagnostic power is high, but our knowledge on etiology and/or effective treatment options still contains important gaps. In the context of day-to-day practice, clinical and preclinical studies, clinicians and basic researchers team up and continuously strive to increase insights into lung disease progression, diagnostic and treatment options. To unravel disease processes and to test novel therapeutic approaches, investigators typically rely on end-stage procedures such as serum analysis, cyto-/chemokine profiles and selective tissue histology from animal models. These techniques are useful but provide only a snapshot of disease processes that are essentially dynamic in time and space. Technology allowing evaluation of live animals repeatedly is indispensable to gain a better insight into the dynamics of lung disease progression and treatment effects. Computed tomography (CT) is a clinical diagnostic imaging technique that can have enormous benefits in a research context too. Yet, the implementation of imaging techniques in laboratories lags behind. In this review we want to showcase the integrated approaches and novel developments in imaging, lung functional testing and pathological techniques that are used to assess, diagnose, quantify and treat lung disease and that may be employed in research on patients and animals. Imaging approaches result in often novel anatomical and functional biomarkers, resulting in many advantages, such as better insight in disease progression and a reduction in the numbers of animals necessary. We here showcase integrated assessment of lung disease with imaging and histopathological technologies, applied to the example of lung fibrosis. Better integration of clinical and preclinical imaging technologies with pathology will ultimately result in improved clinical translation of (therapy) study results.

Development and Progression of Radiologic Abnormalities in Individuals at Risk for Familial Interstitial Lung Disease

Rationale: The preclinical natural history of progressive lung fibrosis is poorly understood.Objectives: Our goals were to identify risk factors for interstitial lung abnormalities (ILA) on high-resolution computed tomography (HRCT) scans and to determine progression toward clinical interstitial lung disease (ILD) among subjects in a longitudinal cohort of self-reported unaffected first-degree relatives of patients with familial interstitial pneumonia.Methods: Enrollment evaluation included a health history and exposure questionnaire and HRCT scans, which were categorized by visual assessment as no ILA, early/mild ILA, or extensive ILA. The study endpoint was met when ILA were extensive or when ILD was diagnosed clinically. Among subjects with adequate study time to complete 5-year follow-up HRCT, the proportion with ILD events (endpoint met or radiographic ILA progression) was calculated.Measurements and Main Results: Among 336 subjects, the mean age was 53.1 (SD, 9.9) years. Those with ILA (early/mild [n = 74] or extensive [n = 3]) were older, were more likely to be ever smokers, had shorter peripheral blood mononuclear cell telomeres, and were more likely to carry the MUC5B risk allele. Self-reported occupational or environmental exposures, including aluminum smelting, lead, birds, and mold, were independently associated with ILA. Among 129 subjects with sufficient study time, 25 (19.4%) had an ILD event by 5 years after enrollment; of these, 12 met the study endpoint and another 13 had radiologic progression of ILA. ILD events were more common among those with early/mild ILA at enrollment (63.3% vs. 6.1%; P < 0.0001).Conclusions: Rare and common environmental exposures are independent risk factors for radiologic abnormalities. In 5 years, progression of ILA occurred in most individuals with early ILA detected at enrollment.

Deep Learning-based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images

Purpose

To develop a deep learning algorithm for the automatic assessment of the extent of systemic sclerosis (SSc)-related interstitial lung disease (ILD) on chest CT images.

Materials and Methods

This retrospective study included 208 patients with SSc (median age, 57 years; 167 women) evaluated between January 2009 and October 2017. A multicomponent deep neural network (AtlasNet) was trained on 6888 fully annotated CT images (80% for training and 20% for validation) from 17 patients with no, mild, or severe lung disease. The model was tested on a dataset of 400 images from another 20 patients, independently partially annotated by three radiologist readers. The ILD contours from the three readers and the deep learning neural network were compared by using the Dice similarity coefficient (DSC). The correlation between disease extent obtained from the deep learning algorithm and that obtained by using pulmonary function tests (PFTs) was then evaluated in the remaining 171 patients and in an external validation dataset of 31 patients based on the analysis of all slices of the chest CT scan. The Spearman rank correlation coefficient (ρ) was calculated to evaluate the correlation between disease extent and PFT results.

Results

The median DSCs between the readers and the deep learning ILD contours ranged from 0.74 to 0.75, whereas the median DSCs between contours from radiologists ranged from 0.68 to 0.71. The disease extent obtained from the algorithm, by analyzing the whole CT scan, correlated with the diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity (ρ = -0.76, -0.70, and -0.62, respectively; P < .001 for all) in the dataset for the correlation with PFT results. The disease extents correlated with diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity were ρ = -0.65, -0.70, and -0.57, respectively, in the external validation dataset (P < .001 for all).

Conclusion

The developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with SSc-related ILD.Supplemental material is available for this article.© RSNA, 2020.

Volumetric assessment of paranasal sinus opacification on computed tomography can be automated using a convolutional neural network

BACKGROUND

Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation, but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time-consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease.

METHODS

Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A nonoverlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund-MacKay (LM) visual scores, pulmonary function test results, and other clinical variables using Spearman correlation and linear regression.

RESULTS

CNN scores were correlated with LM scores (rho = 0.82, p < 0.001) and with forced expiratory volume in 1 second (FEV₁ ) percent predicted (rho = -0.21, p < 0.001), FEV₁ /forced vital capacity ratio (rho = -0.27, p < 0.001), immunoglobulin E (rho = 0.20, p < 0.001), eosinophil count (rho = 0.28, p < 0.001), and exhaled nitric oxide (rho = 0.40, p < 0.001).

CONCLUSION

Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.

Longitudinal functional changes with clinically significant radiographic progression in idiopathic pulmonary fibrosis: are we following the right parameters?

Background

Progression of the disease in idiopathic pulmonary fibrosis (IPF) is difficult to predict, due to its variable and heterogenous course. The relationship between radiographic progression and functional decline in IPF is unclear. We sought to confirm that a simple HRCT fibrosis visual score is a reliable predictor of mortality in IPF, when longitudinally followed; and to ascertain which pulmonary functional variables best reflect clinically significant radiographic progression.

Methods

One-hundred-twenty-three consecutive patients with IPF from 2 centers were followed for an average of 3 years. Longitudinal changes of HRCT fibrosis scores, forced vital capacity (FVC), total lung capacity and diffusing lung capacity for carbon monoxide were considered. HRCTs were scored by 2 chest radiologists. The primary outcome was lung transplant (LTx)-free survival after the follow-up HRCT.

Results

During the follow-up period, 43 deaths and 11 LTx occurred. On average, the HRCT fibrosis score increased significantly, and a longitudinal increase > 7% predicted LTx-free survival significantly, with good specificity, but limited sensitivity. The correlation between radiographic and functional progression was moderately significant. HRCT progression and FVC decline predicted LTx-free survival independently and significantly, with better sensitivity, but worse specificity for a ≥ 5% decline of FVC. However, the area under the curve towards LTx-survival were only 0.61 and 0.62, respectively.

Conclusions

The HRCT fibrosis visual score is a reliable and responsive tool to detect clinically meaningful disease progression. Although no individual pulmonary function test closely reflects radiographic progression, a longitudinal FVC decline improves sensitivity in the detection of clinically significant disease progression. However, the accuracy of these methods remains limited, and better prognostication models need to be found.

Approaching Clinical Trials in Childhood Interstitial Lung Disease and Pediatric Pulmonary Fibrosis

Childhood interstitial lung disease (chILD) comprises a spectrum of rare diffuse lung disorders. chILD is heterogeneous in origin, with different disease manifestations occurring in the context of ongoing lung development. The large number of disorders in chILD, in combination with the rarity of each diagnosis, has hampered scientific and clinical progress within the field. Epidemiologic and natural history data are limited. The prognosis varies depending on the etiology, with some forms progressing to lung transplant or death. There are limited treatment options for patients with chILD. Although U.S. Food and Drug Administration-approved treatments are now available for adult patients with idiopathic pulmonary fibrosis, no clinical trials have been conducted in a pediatric population using agents designed to treat lung fibrosis. This review will focus on progressive chILD disorders and on the urgent need for meaningful objective outcome measures to define, detect, and monitor fibrosis in children.

Imaging research in fibrotic lung disease; applying deep learning to unsolved problems

Over the past decade, there has been a groundswell of research interest in computer-based methods for objectively quantifying fibrotic lung disease on high resolution CT of the chest. In the past 5 years, the arrival of deep learning-based image analysis has created exciting new opportunities for enhancing the understanding of, and the ability to interpret, fibrotic lung disease on CT. Specific unsolved problems for which computer-based imaging analysis might provide solutions include the development of reliable methods for assisting with diagnosis, detecting early disease, and predicting disease behaviour using baseline imaging data. However, to harness this technology, technical and societal challenges must be overcome. Large CT datasets will be needed to power the training of deep learning algorithms. Open science research and collaboration between academia and industry must be encouraged. Prospective clinical utility studies will be needed to test computer algorithm performance in real-world clinical settings and demonstrate patient benefit over current best practice. Finally, ethical standards, which ensure patient confidentiality and mitigate against biases in training datasets, that can be encoded in machine-learning systems will be needed as well as bespoke data governance and accountability frameworks to encourage buy-in from health-care professionals, patients, and the public.