Automated CT Analysis of Major Forms of Interstitial Lung Disease

Warrick score in rheumatoid-arthritis interstitial lung disease: a promising tool for assessing the extent and progression of lung involvement

BACKGROUND

The clinical manifestations and course of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) exhibits considerable heterogeneity. In this study, we aimed to explore radiographic progression over a defined period, employing the Warrick score as a semi-quantitative measure in early RA-ILD, and to assess the associated risk factors for progression.

METHODS

RA-ILD patients underwent consecutive Warrick scoring based on initial high-resolution computed tomography (HRCT) at diagnosis and the first follow-up. Associations between Warrick scores, pulmonary function tests, and patient characteristics were analyzed. The ROC curve assessed the predictive performance of the Warrick score change rate for ILD progression, while multivariable logistic regression analysis identified risk factors for progression.

RESULTS

Significant correlations were found between Warrick scores and age at RA-ILD diagnosis, age at ILD diagnosis, and baseline DAS28-ESR. For the severity score, correlations were r = 0.359, r = 0.372, and r = 0.298 (p = 0.001, p < 0.001, p = 0.014, respectively); for the extent score, r = 0.364, r = 0.318, and r = 0.255 (p = 0.001, p = 0.005, p = 0.038, respectively); and for the total score, r = 0.376, r = 0.367, and r = 0.280 (p < 0.001, p = 0.001, p = 0.022, respectively). Annual changes in severity, extent, and total Warrick scores showed sensitivities of 91-97% and specificities of 98% for predicting progression over a 5-year follow-up. Cut-off values were 0.0278 for the severity score (AUC 0.954), 0.0227 for extent score (AUC 0.976), and 0.0694 for total score (AUC 0.946). Warrick severity, extent, and total scores increased significantly during follow-up. Age > 50 years (OR 7.7; p = 0.028) and baseline usual interstitial pneumonia (UIP) pattern (OR 3.1, p = 0.041) were identified as risk factors for progression.

CONCLUSIONS

Advanced age and UIP pattern were significant risk factors for progression. Warrick scoring may may help predict progression in RA-ILD, particularly through changes in severity, extent, and total scores. Due to the retrospective design and small sample size, further prospective studies with larger cohorts are needed to confirm these findings and validate Warrick scoring as a reliable marker for RA-ILD progression.

Quantitative chest computed tomography predicts mortality in systemic sclerosis: A longitudinal study

OBJECTIVE

Quantitative chest computed tomography (qCT) methods are new tools that objectively measure parenchymal abnormalities and vascular features on CT images in patients with interstitial lung disease (ILD). We aimed to investigate whether the qCT measures are predictors of 5-year mortality in patients with systemic sclerosis (SSc).

METHODS

Patients diagnosed with SSc were retrospectively selected from 2011 to 2022. Patients should have had volumetric high-resolution CTs (HRCTs) and pulmonary function tests (PFTs) performed at baseline and at 24 months of follow-up. The following parameters were evaluated in HRCTs using Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER): ground glass opacities, reticular pattern, honeycombing, and pulmonary vascular volume. Factors associated with death were evaluated by Kaplan‒Meier survival curves and multivariate analysis models. Semiquantitative analysis of the HRCTs images was also performed.

RESULTS

Seventy-one patients were included (mean age, 54.2 years). Eleven patients (15.49%) died during the follow-up, and all patients had ILD. As shown by Kaplan‒Meier curves, survival was worse among patients with an ILD extent (ground glass opacities + reticular pattern + honeycombing) ≥ 6.32%, a reticular pattern ≥ 1.41% and a forced vital capacity (FVC) < 70% at baseline. The independent predictors of mortality by multivariate analysis were a higher reticular pattern (Exp 2.70, 95%CI 1.26-5.82) on qCT at baseline, younger age (Exp 0.906, 95%CI 0.826-0.995), and absolute FVC decline ≥ 5% at follow-up (Exp 15.01, 95%CI 1.90-118.5), but not baseline FVC. Patients with extensive disease (>20% extension) by semiquantitative analysis according to Goh's staging system had higher disease extension on qCT at baseline and follow-up.

CONCLUSION

This study showed that the reticular pattern assessed by baseline qCT may be a useful tool in the clinical practice for assessing lung damage and predicting mortality in SSc.

Diagnostic Approach to Interstitial Lung Diseases Associated with Connective Tissue Diseases

Interstitial lung disorders are a group of respiratory diseases characterized by interstitial compartment infiltration, varying degrees of infiltration, and fibrosis, with or without small airway involvement. Although some are idiopathic (e.g., idiopathic pulmonary fibrosis, idiopathic interstitial pneumonias, and sarcoidosis), the great majority have an underlying etiology, such as systemic autoimmune rheumatic disease (SARD, also called Connective Tissue Diseases or CTD), inhalational exposure to organic matter, medications, and rarely, genetic disorders. This review focuses on diagnostic approaches in interstitial lung diseases associated with SARDs. To make an accurate diagnosis, a multidisciplinary, personalized approach is required, with input from various specialties, including pulmonary, rheumatology, radiology, and pathology, to reach a consensus. In a minority of patients, a definitive diagnosis cannot be established. Their clinical presentations and prognosis can be variable even within subsets of SARDs.

Identification, Monitoring, and Management of Rheumatoid Arthritis-Associated Interstitial Lung Disease

Interstitial lung disease (ILD) is a frequent complication of rheumatoid arthritis (RA) that is associated with a significant increase in mortality. Several risk factors for the development of ILD in patients with RA have been identified, but ILD can still develop in the absence of these risk factors. Screening tools for RA-ILD are required to facilitate early detection of RA-ILD. Close monitoring of patients with RA-ILD for progression is crucial to enable timely implementation of treatment strategies to improve outcomes. Patients with RA are commonly treated with immunomodulatory therapies, although their efficacy in slowing the progression of RA-ILD remains the subject of debate. Clinical trials have shown that antifibrotic therapies slow decline in lung function in patients with progressive fibrosing ILDs, including patients with RA-ILD. The management of patients with RA-ILD should be based on multidisciplinary evaluation of the severity and progression of their ILD and the activity of their articular disease. Close collaboration between rheumatologists and pulmonologists is essential to optimize patient care.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

The effect of CALIPER-derived parameters for idiopathic pulmonary fibrosis in predicting prognosis, progression, and mortality: a systematic review

BACKGROUND

High-resolution computed tomography (HRCT), as the main tool for monitoring idiopathic pulmonary fibrosis (IPF), is characterized by subjective variability among radiologists and insensitivity to subtle changes. Recently, a few studies have aimed to decrease subjective bias by assessing the severity of IPF using computer software, i.e., Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER). However, these studies had diverse research directions. In this review, we systematically assess the effect of CALIPER in the management of IPF.

METHODS

A systematic review was conducted through a search of published studies in PubMed, Web of Science, Cochrane, Embase, Scopus, and CNKI databases from database inception through February 28, 2022. The methodological quality would be evaluated by using Methodological Index for Non-Randomized Studies (MINORS). Narrative synthesis summarized findings by participant characteristics, study design, and associations with outcomes.

RESULTS

Ten studies were included. They evaluated the relationship between CALIPER-derived parameters and pulmonary function test (PFT) and mortality. CALIPER-derived parameters showed a significant correlation with PFT and mortality. Two studies reported that CALIPER could be used to stratify outcomes.

CONCLUSION

CALIPER-derived parameters can be used to evaluate prognosis and mortality. CALIPER-derived parameters combined with composite physiologic index (CPI) or Gender-Age-Physiology (GAP) could help clinicians implement targeted management by refining prognostic stratification. However, research has been constrained by small number of retrospective investigations and sample sizes. Therefore, it is essential to design prospective controlled studies and establish the staging system by CALIPER-derived parameters and combining them with CPI, FVC, or GAP.

CLINICAL RELEVANCE STATEMENT

It is beneficial for clinic to provide objective, sensitive, and accurate indicators of disease progression. It also helps the clinic to develop individualized treatment plans based on the stage of disease progression and provides evaluation of efficacy in drug trials.

KEY POINTS

• Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) is a quantitative CT analysis software that can be used to evaluate the progression of disease on CT. • The CALIPER-derived vessel-related structure shows great performance in the management of idiopathic pulmonary fibrosis. • CALIPER-derived parameters combined with composite physiologic index or Gender-Age-Physiology can be used to refine prognostic stratification.

Present and future perspectives in early diagnosis and monitoring for progressive fibrosing interstitial lung diseases

Progressive fibrosing interstitial lung diseases (PF-ILDs) represent a group of conditions of both known and unknown origin which continue to worsen despite standard treatments, leading to respiratory failure and early mortality. Given the potential to slow down progression by initiating antifibrotic therapies where appropriate, there is ample opportunity to implement innovative strategies for early diagnosis and monitoring with the goal of improving clinical outcomes. Early diagnosis can be facilitated by standardizing ILD multidisciplinary team (MDT) discussions, implementing machine learning algorithms for chest computed-tomography quantitative analysis and novel magnetic-resonance imaging techniques, as well as measuring blood biomarker signatures and genetic testing for telomere length and identification of deleterious mutations in telomere-related genes and other single-nucleotide polymorphisms (SNPs) linked to pulmonary fibrosis such as rs35705950 in the MUC5B promoter region. Assessing disease progression in the post COVID-19 era also led to a number of advances in home monitoring using digitally-enabled home spirometers, pulse oximeters and other wearable devices. While validation for many of these innovations is still in progress, significant changes to current clinical practice for PF-ILDs can be expected in the near future.

CT quantitative analysis of pulmonary changes in rheumatoid arthritis

OBJECTIVE

To explore the application of quantitative computed tomography (CT) in analyses of lung changes in patients with rheumatoid arthritis (RA).

METHODS

A total of 150 clinically diagnosed RA patients underwent chest CT and 150 matched non-smokers subjects with normal chest CT are enrolled. A CT software is applied to analyze CT obtained from both groups. The quantitative indices of emphysema are expressed as the percentage of lung area with attenuation < -950HU to the total lung volume (LAA-950%), and pulmonary fibrosis was expressed as the percentage of lung area with a attenuation of -200 to -700HU to the total lung volume (LAA-200--700%), quantitative indicators of pulmonary vascular include aortic diameter (AD), pulmonary artery diameter (PAD), the ratio of PAD to AD (PAD/AD ratio), the number of blood vessels (TNV), and the cross area of blood vessels (TAV). The receiver operating characteristic (ROC) curve is used to evaluate the ability of these indexes in identifying the changes in the lung in RA patients.

RESULTS

Compared to the control group, the RA group has significantly lower TLV, larger AD, and smaller TNV and TAV (3921±1101 vs. 4490±1046, 33.26±4.20 vs. 32.95±3.76, 13.14±4.93 vs. 17.53±3.34, and 96.89±40.62 vs. 163.32±34.97, respectively, with all p < 0.001). Peripheral vascular indicator TAV has the better ability to identify lung changes in RA patients (area under ROC curve AUC = 0.894) than TNV (AUC = 0.780) or LAA-200 &sim-700% (AUC = 0.705).

CONCLUSION

Quantitative CT can detect changes in lung density distribution and peripheral vascular injury in patients with RA and assess the severity.

3D Single-Breath Chemical Shift Imaging Hyperpolarized Xe-129 MRI of Healthy, CF, IPF, and COPD Subjects

3D Single-breath Chemical Shift Imaging (3D-SBCSI) is a hybrid MR-spectroscopic imaging modality that uses hyperpolarized xenon-129 gas (Xe-129) to differentiate lung diseases by probing functional characteristics. This study tests the efficacy of 3D-SBCSI in differentiating physiology among pulmonary diseases. A total of 45 subjects-16 healthy, 11 idiopathic pulmonary fibrosis (IPF), 13 cystic fibrosis (CF), and 5 chronic obstructive pulmonary disease (COPD)-were given 1/3 forced vital capacity (FVC) of hyperpolarized Xe-129, inhaled for a ~7 s MRI acquisition. Proton, Xe-129 ventilation, and 3D-SBCSI images were acquired with separate breath-holds using a radiofrequency chest coil tuned to Xe-129. The Xe-129 spectrum was analyzed in each lung voxel for ratios of spectroscopic peaks, chemical shifts, and T2* relaxation. CF and COPD subjects had significantly more ventilation defects than IPF and healthy subjects, which correlated with FEV1 predicted (R = -0.74). FEV1 predicted correlated well with RBC/Gas ratio (R = 0.67). COPD and IPF had significantly higher Tissue/RBC ratios than other subjects, longer RBC T2* relaxation times, and greater RBC chemical shifts. CF subjects had more ventilation defects than healthy subjects, elevated Tissue/RBC ratio, shorter Tissue T2* relaxation, and greater RBC chemical shift. 3D-SBCSI may be helpful in the detection and characterization of pulmonary disease, following treatment efficacy, and predicting disease outcomes.

Reticulation pattern without honeycombing on high-resolution CT is associated with the risk of disease progression in interstitial lung diseases

Background

The disease course of idiopathic pulmonary fibrosis (IPF) is progressive and occasionally, other types of interstitial lung disease (ILD) may progress similarly to IPF. This study aimed to evaluate risk factors for disease progression within 24 months in patients with various ILDs.

Methods

This prospective study obtained 97 patients with a suspected ILD who underwent a transbronchial lung cryobiopsy. The extent of several high-resolution computed tomography (HRCT) patterns was assessed. Due to the inclusion criteria the study population presented a low extent of honeycombing and definite usual interstitial pneumonia (UIP) pattern on HRCT suggesting an early stage of ILD. Disease progression within 24 months despite treatment was defined as a relative decline of ≥ 10% in forced vital capacity (FVC), or a relative decline in FVC of ≥ 5% and one of the three additional criteria: (1) a decline in diffusion capacity to carbon monoxide (DLCO) ≥ 15%; (2) increased fibrosis on HRCT; (3) progressive symptoms, or progressive symptoms and increased fibrosis on HRCT. The same definition was utilized in patients with IPF and other ILDs. Risk factors for disease progression were evaluated in a multivariable logistic regression model.

Results

Disease progression was revealed in 52% of the patients with ILD, 51% of the patients with IPF, and 53% of the patients with other types of ILD. A high extent of reticulation on HRCT (Odds ratio [OR] 3.11, 95% Confidence interval [CI] 1.21–7.98, P = 0.019) and never smoking (OR 3.11, CI 1.12–8.63, P = 0.029) were associated with disease progression whereas platelet count (OR 2.06 per 100 units increase, CI 0.96–4.45, P = 0.065) did not quite reach statistical significance.

Conclusion

Higher extent of reticulation on HRCT and never smoking appeared to associate with the risk of disease progression within 24 months in ILD patients without honeycombing. Approximately half of the patients with ILD revealed disease progression, and similar proportions were observed in patients with IPF and in other types of ILD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02105-9.

A Novel Method for Lung Image Processing Using Complex Networks

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in three dimensions: emphysema, ground glass opacity, and consolidation. This method was evaluated on a 60-patient lot and the results showed a clear, quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

Quantitative CT and machine learning classification of fibrotic interstitial lung diseases

OBJECTIVES

To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models' performance.

METHODS

We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances.

RESULTS

The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction.

CONCLUSIONS

QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification.

KEY POINTS

• Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance.

Correlation of respiratory oscillometry with CT image analysis in a prospective cohort of idiopathic pulmonary fibrosis

Background

Markers of idiopathic pulmonary fibrosis (IPF) severity are based on measurements of forced vital capacity (FVC), diffusing capacity (DLCO) and CT. The pulmonary vessel volume (PVV) is a novel quantitative and independent prognostic structural indicator derived from automated CT analysis. The current prospective cross-sectional study investigated whether respiratory oscillometry provides complementary data to pulmonary function tests (PFTs) and is correlated with PVV.

Methods

From September 2019 to March 2020, we enrolled 89 patients with IPF diagnosed according to international guidelines. We performed standard spectral (5–37 Hz) and novel intrabreath tracking (10 Hz) oscillometry followed by PFTs. Patients were characterised with the gender-age-physiology (GAP) score. CT images within 6 months of oscillometry were analysed in a subgroup (26 patients) using automated lung texture analysis. Correlations between PFTs, oscillometry and imaging variables were investigated using different regression models.

Findings

The cohort (29F/60M; age=71.7±7.8 years) had mild IPF (%FVC=70±17, %DLCO=62±17). Spectral oscillometry revealed normal respiratory resistance, low reactance, especially during inspiration at 5 Hz (X5in), elevated reactance area and resonance frequency. Intrabreath oscillometry identified markedly low reactance at end-inspiration (XeI). XeI and X5in strongly correlated with FVC (r²=0.499 and 0.435) while XeI was highly (p=0.004) and uniquely correlated with the GAP score. XeI and PVV exhibited the strongest structural-functional relationship (r²=0.690), which remained significant after adjusting for %FVC, %DLCO and GAP score.

Interpretation

XeI is an independent marker of IPF severity that offers additional information to standard PFTs. The data provide a cogent rationale for adding oscillometry in IPF assessment.

Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis

INTRODUCTION

Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS).

MATERIALS AND METHODS

We performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care.

RESULTS

There was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001).

CONCLUSION

Our study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality.

KEY POINTS

• The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients. • VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001. • Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality.