Lung CT densitometry in systemic sclerosis: correlation with lung function, exercise testing, and quality of life

Histogram-Based Densitometry Index to Assess the Severity of Interstitial Lung Disease in Systemic Sclerosis in Standard and Low-Dose Computed Tomography

OBJECTIVE

Mean lung attenuation, skewness, and kurtosis are histogram-based densitometry variables that quantify systemic sclerosis-associated interstitial lung disease (SSc-ILD) and were recently merged into a computerized integrated index (CII). Our work tested the CII in low-dose 9-slice (reduced) and standard high-resolution computed tomography (CT) scans to evaluate extensive SSc-ILD and predict mortality.

METHODS

CT scans from patients with SSc-ILD were assessed using the software Horos to compute standard and reduced CIIs. Extensive ILD was determined following the Goh staging system. The association between CIIs and extensive ILD was analyzed with a generalized estimating equation regression model, the predictive ability of CIIs by the area under the receiver-operation characteristic curve (AUC), and the association between CIIs and death by Kaplan-Meier analysis.

RESULTS

Among 243 patients with standard and reduced CT scans available, 157 CT scans from 119 patients with SSc-ILD constituted the derivation cohort. The validation cohort included 116 standard and 175 reduced CT scans. Both CIIs from standard (odds ratio [OR] 0.53, 95% CI 0.37-0.75; AUC 0.77, 95% CI 0.68-0.87) and reduced CT scans (OR 0.54, 95% CI 0.35-0.82; AUC 0.78, 95% CI 0.70-0.87) were significantly associated with extensive ILD. A threshold of CII ≤ -0.96 for standard CT scans and CII ≤ -1.85 for reduced CT scans detected extensive ILD with high sensitivity in both derivation and validation cohorts. Extensive ILD according to Goh staging (OR 2.94, 95% CI 1.10-7.82) and standard CII ≤ -0.96 (OR 1.78, 95% CI 1.24-2.56) significantly predicted mortality; a marginal P value was observed for reduced CII ≤ -1.85 (OR 1.27, 95% CI 0.93-1.75).

CONCLUSION

Thresholds for both standard and reduced CII to identify extensive ILD were developed and validated, with an additional association with mortality. CIIs might help in clinical practice when radiology expertise is missing.

CT evaluation of interstitial lung disease related to systemic sclerosis: visual versus automated assessment. A systematic review

AIM

To identify similarities and differences between visual (VA) and automated assessment (AA) of systemic sclerosis-related interstitial lung disease (SSc-ILD) at chest computed tomography (CT) in terms of clinical applicability.

MATERIALS AND METHODS

Medline, Embase, and Web of Science were searched to identify all studies investigating VA and AA for SSc-ILD assessment, from inception to 31 July 2022. Exclusion criteria were manuscripts not in English, absence of full-text, reviews, diseases other than ILD in SSc, CT not analysed with both VA and AA, VA and AA not adopted for the same purpose or not compared, overlap syndromes, SSc-ILD data not extractable, and studies with <10 patients.

RESULTS

Ten full-text studies (804 patients) were included. The most adopted VAs were the Warrick or Goh score (four studies each), while densitometry (eight studies) or lung texture analysis (LTA, two studies) were utilised as AAs. The main field of investigation was the correlation with baseline pulmonary function tests (PFT, six studies). Warrick VA showed lower correlations compared to densitometry, while Goh VA demonstrated more heterogeneous results. Compared to LTA, Goh VA obtained lower correlations with lung volumes but similar or stronger coefficients with alveolar diffusibility.

CONCLUSIONS

VA and AA may show heterogeneous results comparing their correlations with PFT, probably depending on the specific analysis adopted for each method. More data are needed on VA versus LTA. Comparisons between VA and AA regarding correlation with PFT follow-up and as prognostic elements, or for disease monitoring, are lacking. AAs in progressive fibrosis diagnosis remain to be tested.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology"

Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.

Clinicopathological Outlines of Post-COVID-19 Pulmonary Fibrosis Compared with Idiopathic Pulmonary Fibrosis

This review brings together the current knowledge regarding the risk factors and the clinical, radiologic, and histological features of both post-COVID-19 pulmonary fibrosis (PCPF) and idiopathic pulmonary fibrosis (IPF), describing the similarities and the disparities between these two diseases, using numerous databases to identify relevant articles published in English through October 2022. This review would help clinicians, pathologists, and researchers make an accurate diagnosis, which can help identify the group of patients selected for anti-fibrotic therapies and future therapeutic perspectives.

Assessment of cardiopulmonary manifestations and its correlation with semi-quantitative scoring of high-resolution computed tomography in patients with autoimmune rheumatic diseases

PURPOSE

Autoimmune rheumatic diseases (ARD) are groups of diseases that are commonly associated with cardiac and pulmonary manifestations and may affect the morbidity and mortality of the patients. The study aimed to the assessment of cardiopulmonary manifestations and their correlation with the semi-quantitative scoring of high-resolution computed tomography (HRCT) in ARD patients.

METHODS AND PATIENTS

30 patients with ARD were included in the study (mean age 42.2 ± 9.76 years) [10 patients were scleroderma (SSc), 10 patients were rheumatoid arthritis (RA), and 10 patients were systemic lupus erythematosus (SLE)]. They all met the diagnostic criteria of the American College of Rheumatology and underwent spirometry, echocardiography, and chest HRCT. The HRCT was assessed by a semi-quantitative score for parenchymal abnormalities. Correlation between HRCT lung scores and: inflammatory markers, lung volumes in spirometry, and echocardiographic indices has been performed.

RESULTS

The total lung score (TLS) by HRCT was 14.8 ± 8.78 (mean ± SD), ground glass opacity score (GGO) was 7.20 ± 5.79 (mean ± SD) and fibrosis lung score (F) was 7.63 ± 6.05 (mean ± SD). TLS correlated significantly with ESR (r 0.528, p 0.003), CRP (r 0.439, p 0.015), PaO2 (r -0.395, P 0.031) FVC% (r -0.687, p 0.001), and echocardiographic Tricuspid E (r -0.370, p 0.044), Tricuspid E/è (r -0.397,p 0.03), ESPAP (r 0.459,p 0.011), TAPSE (r -0.405, p 0.027), MPI-TDI (r -0.428, p 0.018) and RV Global strain(r -0.567, p 0.001). GGO score correlated significantly with ESR (r 0.597, p 0.001), CRP (r 0.473, p 0.008), FVC% (r -0.558, p 0.001), and RV Global strain(r -0.496, p 0.005). F score correlated significantly with FVC% (r -0.397, p 0.030), Tricuspid E/è (r -0.445, p 0.014), ESPAP (r 0.402, p 0.028), and MPI-TDI (r -0.448, p 0.013).

CONCLUSION

The total lung score and GGO score in ARD were found to be consistently significantly correlated with FVC% predicted, PaO2, inflammatory markers, and RV functions. Fibrotic score correlated with ESPAP. Therefore, in a clinical setting, most clinicians who monitor patients suffering from ARD should concern with the applicability of semiquantitative HRCT scoring in clinical practice.

The Effects of Statins on Respiratory Symptoms and Pulmonary Fibrosis in COVID-19 Patients with Diabetes Mellitus: A Longitudinal Multicenter Study

The aim of this prospective cohort study was to explore the effect of statins on long-term respiratory symptoms and pulmonary fibrosis in coronavirus disease 2019 (COVID-19) patients with diabetes mellitus (DM). Patients were recruited from three tertiary hospitals, categorized into Statin or Non-statin groups, and assessed on days 0, 28, and 90 after symptoms onset to record the duration of symptoms. Pulmonary fibrosis was scored at baseline and follow-up time points by high-resolution computed tomography scans. Each group comprised 176 patients after propensity score matching. Data analysis revealed that the odds of having cough and dyspnea were significantly higher in the Non-statin group compared to the Statin group during the follow-up period. Overall, there was no significant difference in the change in pulmonary fibrosis score between groups. However, Non-statin patients with > 5 years of DM were more likely to exhibit a significantly higher fibrosis score during the follow-up period as compared to their peers in the Statin group. Our results suggest that the use of statins is associated with a lower risk of developing chronic cough and dyspnea in diabetic patients with COVID-19, and may reduce pulmonary fibrosis associated with COVID-19 in patients with long-term (> 5 years) DM.

Heterogeneity of Lung Density in Patients With Chronic Thromboembolic Pulmonary Hypertension (CTEPH)

RATIONALE AND OBJECTIVES

Pulmonary endarterectomy (PEA) is one of the most effective treatments for chronic thromboembolic pulmonary hypertension (CTEPH). Right heart catheterization (RHC) is the gold standard assessment for pulmonary circulatory dynamics. However, computed tomography (CT) is less invasive than RHC and can elucidate some of the morphological changes caused by thromboembolism. We hypothesized that CT could facilitate the evaluation of heterogeneous pulmonary perfusion. This study investigated whether CT imaging features reflect the disease severity and changes in pulmonary circulatory dynamics in patients with CTEPH before and after PEA.

MATERIALS AND METHODS

This retrospective study included 58 patients with CTEPH who underwent PEA. Pre-PEA and post-PEA CT images were assessed for heterogeneity using CT texture analysis (CTTA). The CT parameters were compared with the results of the RHC and other clinical indices and analyzed with receiver operating characteristic curves analysis for patients with and without residual pulmonary hypertension (PH) (post-PEA mean pulmonary artery pressure ≥ 25 mmHg).

RESULTS

CT measurements reflecting heterogeneity were significantly correlated with mean pulmonary artery pressure. Kurtosis, skewness, and uniformity were significantly lower, and entropy was significantly higher in patients with residual PH than patients without residual PH. Area under the curve values of pre-PEA and post-PEA entropy between patients with and without residual PH were 0.71 (95% confidence interval 0.57-0.84) and 0.75 (0.63-0.88), respectively.

CONCLUSION

Heterogeneity of lung density might reflect pulmonary circulatory dynamics, and CTTA for heterogeneity could be a less invasive technique for evaluation of changes in pulmonary circulatory dynamics in patients with CTEPH undergoing PEA.

How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients’ care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

Assessing predictors of rheumatoid arthritis-associated interstitial lung disease using quantitative lung densitometry

OBJECTIVE

To assess predictors of subclinical RA-associated interstitial lung disease (RA-ILD) using quantitative lung densitometry (qLD).

METHODS

RA patients underwent multi-detector row CT scanning at baseline and after an average of 39 months. Scans were analysed with qLD for the percentage of lung parenchyma with high attenuation areas (%HAA: the percentage of voxels of -600 to -250 Hounsfield units). Additionally, a pulmonary radiologist calculated an expert radiologist scoring (ERS) for RA-ILD features. Generalized linear models were used to identify indicators of baseline %HAA and predictors of %HAA change.

RESULTS

Baseline %HAA was assessed in 193 RA patients and 106 had repeat qLD assessment. %HAA was correlated with ERS (Spearman's rho = 0.261; P < 0.001). Significant indicators of high baseline %HAA (>10% of lung parenchyma with high attenuation) included female sex, higher pack-years of smoking, higher BMI and anti-CCP ≥200 units, collectively contributing an area under the receiver operator curve of 0.88 (95% CI 0.81, 0.95). Predictors of %HAA increase, occurring in 49% with repeat qLD, included higher baseline %HAA, presence of mucin 5B (MUC5B) minor allele and absence of HLA-DRB1 shared epitope (area under the receiver operator curve = 0.69; 95% CI 0.58, 0.79). The association of the MUC5B minor allele with %HAA change was higher among men and those with higher cumulative smoking. Within the group with increased %HAA, anti-CCP level was significantly associated with a greater increase in %HAA.

CONCLUSIONS

%HAA, assessed with qLD, was linked to several known risk factors for RA-ILD and may represent a more quantitative method to identify RA-ILD and track progression than expert radiologist interpretation.

Early Radiographic Progression of Scleroderma: Lung Disease Predicts Long-term Mortality

BACKGROUND

Radiographic end points commonly are included in therapeutic trials for systemic sclerosis (SSc)-interstitial lung disease (ILD); however, the relationship between these outcomes and long-term mortality is unclear.

RESEARCH QUESTION

Do short-term changes in radiographic measures of ILD predict long-term survival in patients with SSc?

STUDY DESIGN AND METHODS

The Scleroderma Lung Study (SLS) I and II evaluated the safety and efficacy of cyclophosphamide (in SLS I and II) and mycophenolate mofetil (in SLS II) for the treatment of SSc-ILD. Changes in the extent of ILD over time were assessed on high-resolution CT scans of the chest by quantitative image analysis, an approach that applies a computer-based algorithm to assess changes in the radiographic extent of ILD objectively. Participants subsequently were followed for up to 12 years (SLS I) and 8 years (SLS II). Cox proportional hazards models determined whether the change in the quantitative radiographic extent of ILD predicted survival, adjusting for other known predictors of survival.

RESULTS

Among SLS I and II participants, 82 and 90 had follow-up imaging scans, respectively, and were included in the analysis. Participants in both trials who showed an increase in the total quantitative radiographic extent of ILD scores of ≥ 2% at 12 months (SLS I) or 24 months (SLS II) experienced significantly worse long-term survival than those with change scores of < 2% (P ≤ .01, log-rank test). In the multivariate Cox models, radiographic progression remained associated with worse long-term survival in SLS I (P = .089) and SLS II (P = .014).

INTERPRETATION

Data from two independent clinical trial cohorts with extensive long-term follow-up demonstrated that radiographic progression of ILD over 12 to 24 months, in both treatment and placebo arms, can predict increased risk for long-term mortality in patients with SSc. These findings suggest that radiographic end points may serve as surrogates for mortality in SSc-ILD.

Quantitative Assessment of Airway Changes in Fibrotic Interstitial Lung Abnormality Patients by Chest CT According to Cumulative Cigarette Smoking

Purpose: The aim of this study was to evaluate the role of Pi10 in patients with fibrotic interstitial lung abnormality (fibrotic ILA) in a chest CT, according to cumulative cigarette smoking. Methods: We retrospectively assessed 54 fibrotic ILA patients and 18 healthy non-smokers (control) who underwent non-enhanced CT and pulmonary function tests. We quantitatively analyzed airway changes (the inner luminal area, airway inner parameter, airway wall thickness, Pi10, skewness, and kurtosis) in the chest CT of fibrotic ILA patients, and the fibrotic ILA patients were categorized into groups based on pack-years: light, moderate, heavy. Airway change data and pulmonary function tests among the three groups of fibrotic ILA patients were compared with those of the control group by one-way ANOVA. Results: Mean skewness (2.58 ± 0.36) and kurtosis (7.64 ± 2.36) in the control group were significantly different from those of the fibrotic ILA patients (1.89 ± 0.37 and 3.62 ± 1.70, respectively, p < 0.001). In fibrotic ILA group, only heavy smokers had significantly increased Pi10 (mean increase 0.04, p = 0.013), increased airway wall thickness of the segmental bronchi (mean increase 0.06 mm, p = 0.005), and decreased lung diffusing capacity for carbon monoxide (p = 0.023). Conclusion: Pi10, as a biomaker of quantitative CT in fibrotic ILA patients, can reveal that smoking affects airway remodeling.

A Convolutional Neural Network Approach to Quantify Lung Disease Progression in Patients with Fibrotic Hypersensitivity Pneumonitis (HP)

Rationale and Objectives To evaluate associations between longitudinal changes of quantitative CT parameters and spirometry in patients with fibrotic hypersensitivity pneumonitis (HP). Materials and Methods Serial CT images and spirometric data were retrospectively collected in a group of 25 fibrotic HP patients. Quantitative CT analysis included histogram parameters (median, interquartile range, skewness, and kurtosis) and a pretrained convolutional neural network (CNN)-based textural analysis, aimed at quantifying the extent of consolidation (C), fibrosis (F), ground-glass opacity (GGO), low attenuation areas (LAA) and healthy tissue (H). Results At baseline, FVC was 61(44-70) %pred. The median follow-up period was 1.4(0.8-3.2) years, with 3(2-4) visits per patient. Over the study, 8 patients (32%) showed a FVC decline of more than 5%, a significant worsening of all histogram parameters (p≤0.015) and an increased extent of fibrosis via CNN (p=0.038). On histogram analysis, decreased skewness and kurtosis were the parameters most strongly associated with worsened FVC (respectively, r2=0.63 and r2=0.54, p<0.001). On CNN classification, increased extent of fibrosis and consolidation were the measures most strongly correlated with FVC decline (r2=0.54 and r2=0.44, p<0.001). Conclusion CT histogram and CNN measurements provide sensitive measures of functional changes in fibrotic HP patients over time. Increased fibrosis was associated with FVC decline, providing index of disease progression. CNN may help improve fibrotic HP follow-up, providing a sensitive tool for progressive interstitial changes, which can potentially contribute to clinical decisions for individualizing disease management.

High-Resolution Computed Tomography: Lights and Shadows in Improving Care for SSc-ILD Patients

The diagnosis and classification of systemic sclerosis-associated interstitial lung disease (SSc-ILD) is essential to improve the prognosis of systemic sclerosis (SSc) patients. The risk-stratification of disease severity and follow-up requires a multidisciplinary approach, integrating high-resolution computed tomography (HRTC) of the lung, pulmonary function tests (PFT), along with clinical and symptomatic evaluations. The use of HRCT in detecting SSc-ILD is not so much based on a definitive validation, but rather reflects the widespread clinician recognition of dissatisfaction with other modalities. However, due to the heterogeneity of SSc-ILD and the potential absence of symptoms in early or mild disease, it is prudent to consider as many parameters as possible in the assessment and monitoring of newly diagnosed patients. An early diagnosis meets the primary goal, i.e., the prevention of disease progression. The current first line treatment regimens are mainly centered on immunosuppressive therapy. This review assesses the role HRCT plays in optimizing care and improving clinical outcomes in SSc-ILD patients.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

BACKGROUND

The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD).

PURPOSE

To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD.

MATERIAL AND METHODS

A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as "Stable" (n = 188), "Worse" (n = 98) and "Improved" (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans.

RESULTS

Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r² ≤0.42; P < 0.05).

CONCLUSION

ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups.

Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed.

Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0–5) vs. 0 (0–4), p = 0.001] and reticulation [0 (0–5.25) vs 0 (0–0), p = 0.001] as well as higher total score [7 (5–12.25) vs. 6 (5–7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0–0) vs. 0.09 (0–0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0–4) vs. 0 (0–4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (−750, −300) and within HU (−300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (−750), and within HU (−750, −300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences.

Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

Association of quantitative computed tomography ındices with lung function and extent of pulmonary fibrosis in patients with systemic sclerosis

OBJECTIVES

The aim was to investigate the discriminative value of a wide range of quantitative computed tomography (qCT) parameters in systemic sclerosis (SSc) patients with and without pulmonary fibrosis (PF) and their association with pulmonary function tests (PFTs) and visual fibrosis scores (VFS).

METHOD

Thoracic high-resolution computed tomography (HRCT) images of SSc patients with and without PF were analyzed with Vitrea® Advanced Visualization software. The mean lung attenuation (MLA), skewness, kurtosis, and threshold-based volumes [low-density volume (LDV), medium-density volume (MDV), and high-density volume (HDV)] derived from the attenuation histograms of the right and left lungs were evaluated separately. Visual scores were measured semi-quantitatively and the overall extent of pulmonary parenchymal abnormality was calculated.

RESULTS

Forty-one SSc patients with PF (85.4% female; mean age 50.4 ± 15.6 years) were compared with 94 without PF (88.3% female; mean age 50 ± 11.5 years). All qCT parameters were significantly different between those with and without PF (p < 0.05). Amongst the qCT measurements, R-MLA, L-MLA, R-MDV, L-MDV, and left total lung volume (L-TLV) correlated with all three of forced vital capacity, carbon monoxide diffusion capacity, and VFS, even after adjustment for sex and age (|r|> 0.300 and p < 0.05). R-MLA, L-MLA, R-HDV/TLV, and L-HDV/TLV exhibited diagnostic accuracy in discriminating patients with PF (AUC value > 0.7).

CONCLUSION

QCT parameters differentiated SSc patients with PF from the ones without and showed a good correlation with VFS. With the application of user-friendly and less operator-dependent software, qCT analysis may become an objective tool for analysis of PF in SSc, complementary to PFTs and VFS. Key Points • Quantitative computed tomography parameters can accurately and objectively differentiate between SSc patients with and without PF. • Furthermore, in SSc patients with fibrosis, a moderate to a high correlation was identified between many of the qCT parameters, PFT results, and VFS.

The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT

The characteristics and evolution of pulmonary fibrosis in patients with coronavirus disease 2019 (COVID-19) have not been adequately studied. AI-assisted chest high-resolution computed tomography (HRCT) was used to investigate the proportion of COVID-19 patients with pulmonary fibrosis, the relationship between the degree of fibrosis and the clinical classification of COVID-19, the characteristics of and risk factors for pulmonary fibrosis, and the evolution of pulmonary fibrosis after discharge. The incidence of pulmonary fibrosis in patients with severe or critical COVID-19 was significantly higher than that in patients with moderate COVID-19. There were significant differences in the degree of pulmonary inflammation and the extent of the affected area among patients with mild, moderate and severe pulmonary fibrosis. The IL-6 level in the acute stage and albumin level were independent risk factors for pulmonary fibrosis. Ground-glass opacities, linear opacities, interlobular septal thickening, reticulation, honeycombing, bronchiectasis and the extent of the affected area were significantly improved 30, 60 and 90 days after discharge compared with at discharge. The more severe the clinical classification of COVID-19, the more severe the residual pulmonary fibrosis was; however, in most patients, pulmonary fibrosis was improved or even resolved within 90 days after discharge.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Systemic sclerosis: correlation between lung abnormalities on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs)

Background

Systemic sclerosis is a connective tissue disease that affects multiple systems and causes fibrosis of the skin and internal organs. There are two ways in which the lungs can be involved in patients with systemic sclerosis, either isolated pulmonary hypertension or interstitial lung fibrosis. The purpose of this study is to correlate the high resolution CT findings with pulmonary function tests in patients with systemic sclerosis to evaluate the severity of lung changes.

Results

Significant inverse correlations were found between the maximal extent radiological score, maximal severity radiological score as well as total (global) radiological score on one hand and the pulmonary function tests on the other hand

Conclusion

The combination of high resolution CT and pulmonary function tests are recommended for better assessment of the extent and severity of systemic sclerosis associated interstitial lung disease.

Computer-aided analysis in evaluation and grading of interstitial lung diseases in correlation with CT-based visual scoring and pulmonary function tests

Background

Interstitial lung diseases (ILDs) represent a large group of more than 200 different entities. High resolution computed tomography (HRCT) is accepted as the gold standard imaging modality in the diagnosis of ILD. The visual-based scoring offers an advantage in finding a specific type of ILD. Computer-aided CT attenuation histogram is another way of characterizing and quantifying diffuse lung disease. The histogram analysis (HIST) consists of calculating skewness, kurtosis, and mean lung density to quantify lung disease and monitor progression. The aim of our study was to investigate the value of computer-aided analysis of HRCT for interstitial lung diseases in correlation with scoring and pulmonary function tests.

Results

This prospective study included 50 patients with suspected ILD. The mean age of patients was 46.7 years ± 12.5. Mean forced expiratory volume FEV1 was 63.6 ± 20.9. HRCT examination was done for all patients followed by CT-based visual scaling. Most of the studied patients (43.3%) had a CT visual semi-quantitative scoring ranged between 40 and 64. CT-based lung density histograms (LDH) were obtained for all patients using the 3D Slicer Software (Chest Imaging Platform). There was a significant difference between patient’s groups of different (mild, moderate, and severe) grades of ILD according to FEV1 regarding MLD, skewness, and kurtosis of corresponding CT-based density histograms (p values < 0.001). More significant and higher correlation was observed between computerized aided CT quantified mean lung densities (MLD) and (FEV1) (p value < 0.001 and r = − 0.570). The ROC curve analysis demonstrated good performance for CT visual scoring with PFT (AUC = 0.71); a cutoff scoring 15 or higher was associated with best sensitivity (75%) and specificity (100%). Meanwhile, ROC curve analysis for MLD and FEV1 demonstrated an excellent performance for computer-based CT quantification (AUC = 0.85) with a value of − 769 HU which increased sensitivity to 65% and specificity to 100%.

Conclusion

Visual-based scoring techniques offer an advantage in finding a specific type of ILD. Computer-based quantification system could be a means for accurately monitoring the disease progression or response to therapy.

Comparative analysis of density histograms and visual scores in incremental and volumetric high-resolution computed tomography of the chest in idiopathic pulmonary fibrosis patients

Background

Volumetric high-resolution computed tomography (HRCT) of the chest has recently replaced incremental CT in the diagnostic workup of idiopathic pulmonary fibrosis (IPF). Concomitantly, visual and quantitative scores have been proposed for disease extent assessment to ameliorate disease management.

Purpose

To compare the performance of density histograms (mean lung attenuation, skewness, and kurtosis) and visual scores, along with lung function correlations, in IPF patients submitted to incremental or volumetric thorax HRCT.

Material and methods

Clinical data and CT scans of 89 newly diagnosed and therapy-naive IPF patients were retrospectively evaluated.

Results

Forty-six incremental and 43 volumetric CT scans were reviewed. No differences of density histograms and visual scores estimates were found by comparing two HRCT techniques, with an optimal inter-operator agreement (concordance correlation coefficient >0.90 in all instances). Single-breath diffusing lung capacity for carbon monoxide (DLCO_sb) was inversely related with the Best score (r = −00.416; p = 0.014), the Kazerooni fibrosis extent (r = −0.481; p = 0.004) and the mean lung attenuation (r = −0.382; p = 0.026), while a positive correlation was observed with skewness (r = 0.583; p = 0.001) and kurtosis (r = 0.543; p = 0.001) in the incremental HRCT sub-group. Similarly, in the volumetric CT sub-cohort, DLCO_sb was significantly associated with skewness (r = 0.581; p = 0.007) and kurtosis (r = 0.549; p = 0.018). Correlations with visual scores were not confirmed. Forced vital capacity significantly related to all density indices independently on HRCT technique.

Conclusions

Density histograms and visual scores similarly perform in incremental and volumetric HRCT. Density quantification displays an optimal reproducibility and proves to be superior to visual scoring as more strongly correlated with lung function.

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.

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.

Quantitative computed tomography assessment for systemic sclerosis-related interstitial lung disease: comparison of different methods

OBJECTIVES

To compare the previously defined six different histogram-based quantitative lung assessment (QLA) methods on high-resolution CT (HRCT) in patients with systemic sclerosis (SSc)-related interstitial lung disease (ILD).

METHODS

The HRCT images of SSc patients with ILD were reviewed, and the visual ILD score (semiquantitative) and the severity of ILD (limited or extensive) were calculated. The QLA score of ILD was evaluated using the previously defined six different methods and parameters (different lung attenuation ranges, skewness, kurtosis, mean lung attenuation, and standard deviation [SD]). Pulmonary function tests (PFTs) were also performed on all patients. Relationships among variables were evaluated using Spearman's correlation coefficient (r). Diagnostic performance of quantitative methods for the ability to differentiate the limited from extensive ILD was calculated using ROC analysis.

RESULTS

Fifty-five patients were included in the study. There was a significant correlation between all quantitative and semiquantitative measurement results (p < 0.0001). The QLA scores revealed a significant correlation with PFT results. The kurtosis value of the voxels between - 200 and - 1024 Hounsfield unit (HU) (Method-5) showed the best correlation with semiquantitative evaluation (r = - 0.740, p < 0.0001). The ROC analysis demonstrated the best performance of SD of the voxels between - 400 and - 950 HU (Method-6) for histogram analysis method and Method-3 (voxels between - 260 and - 600 HU were calculated as ILD) for CT density cutoff methods.

CONCLUSIONS

All the QLA methods are applicable in assessing the ILD score in SSc patients and have potential importance to differentiate limited from extensive ILD.

KEY POINTS

• Quantitative interstitial lung disease assessment helps clinicians to assess systemic sclerosis patients with interstitial lung disease. • Quantitative lung assessment methods are applicable in assessing the interstitial lung disease score in systemic sclerosis patients. • Quantitative lung assessment methods have potential importance in the management of patients.

Performance of a new quantitative computed tomography index for interstitial lung disease assessment in systemic sclerosis

Quantitative high resolution computed tomography (HRCT) may objectively assess systemic sclerosis (SSc)-interstitial lung disease (ILD) extent, using three basic densitometric measures: mean lung attenuation (MLA), skewness, and kurtosis. This prospective study aimed to develop a composite index - computerized integrated index (CII) – that accounted for MLA, skewness, and kurtosis by means of Principal Component Analysis over HRCTs of 83 consecutive SSc subjects, thus eliminating redundancies. Correlations among CII, cardiopulmonary function and immune-inflammatory biomarkers (e.g. sIL-2Rα and CCL18 serum levels) were explored. ILD was detected in 47% of patients at visual HRCT assessment. These patients had worse CII values than patients without ILD. The CII correlated with lung function at both baseline and follow-up, and with sIL-2Rα and CCL18 serum levels. The best discriminating CII value for ILD was 0.1966 (AUC = 0.77; sensitivity = 0.81 [95%CI:0.68–0.92]; specificity = 0.66 [95%CI:0.52–0.80]). Thirty-four percent of patients without visual trace of ILD had a CII lower than 0.1966, and 67% of them had a diffusing lung capacity for CO <80% of predicted. We showed that this new composite CT index for SSc-ILD assessment correlates with both lung function and immune-inflammatory parameters and could be sufficiently sensitive for capturing early lung density changes in visually ILD-free patients.

Automated CT quantification methods for the assessment of interstitial lung disease in collagen vascular diseases: A systematic review

Interstitial lung disease (ILD) is highly prevalent in collagen vascular diseases and reduction of ILD is an important therapeutic target. To that end, reliable quantification of pulmonary disease severity is of great significance. This study systematically reviewed the literature on automated computed tomography (CT) quantification methods for assessing ILD in collagen vascular diseases. PRISMA-DTA guidelines for systematic reviews were used and 19 original research articles up to January 2018 were included based on a MEDLINE/Pubmed and Embase search. Quantitative CT methods were categorized as histogram assessment (12 studies) or pattern/texture recognition (7 studies). R² for correlation with visual ILD scoring ranged from 0.143 (p < 0.01) to 0.687 (p < 0.0001), for FVC from 0.048 (p < 0.0001) to 0.504 (p < 0.0001) and for DLCO from 0.015 (p = 0.61) to 0.449 (p < 0.0001). Automated CT methods are independent of reader's expertise and are a promising tool in the quantification of ILD in collagen vascular disease patients.

Pulmonary function tests as outcomes for systemic sclerosis interstitial lung disease

Interstitial lung disease (ILD) is the leading cause of morbidity and mortality in systemic sclerosis (SSc). We performed a systematic review to characterise the use and validation of pulmonary function tests (PFTs) as surrogate markers for systemic sclerosis-associated interstitial lung disease (SSc-ILD) progression.Five electronic databases were searched to identify all relevant studies. Included studies either used at least one PFT measure as a longitudinal outcome for SSc-ILD progression (i.e. outcome studies) and/or reported at least one classical measure of validity for the PFTs in SSc-ILD (i.e. validation studies).This systematic review included 169 outcome studies and 50 validation studies. Diffusing capacity of the lung for carbon monoxide (DLCO) was cumulatively the most commonly used outcome until 2010 when it was surpassed by forced vital capacity (FVC). FVC (% predicted) was the primary endpoint in 70.4% of studies, compared to 11.3% for % predicted DLCO Only five studies specifically aimed to validate the PFTs: two concluded that DLCO was the best measure of SSc-ILD extent, while the others did not favour any PFT. These studies also showed respectable validity measures for total lung capacity (TLC).Despite the current preference for FVC, available evidence suggests that DLCO and TLC should not yet be discounted as potential surrogate markers for SSc-ILD progression.

Diagnostic criteria, severity classification and guidelines of systemic sclerosis

Several effective drugs have been identified for the treatment of systemic sclerosis (SSc). However, in advanced cases, not only their effectiveness is reduced but they may be also harmful due to their side-effects. Therefore, early diagnosis and early treatment is most important for the treatment of SSc. We established diagnostic criteria for SSc in 2003 and early diagnostic criteria for SSc in 2011, for the purpose of developing evaluation of each organ in SSc. Moreover, in November 2013, the American College of Rheumatology and the European Rheumatology Association jointly developed new diagnostic criteria for increasing their sensitivity and specificity, so we revised our diagnostic criteria and severity classification of SSc. Furthermore, we have revised the clinical guideline based on the newest evidence. In particular, the clinical guideline was established by clinical questions based on evidence-based medicine according to the New Minds Clinical Practice Guideline Creation Manual (version 1.0). We aimed to make the guideline easy to use and reliable based on the newest evidence, and to present guidance as specific as possible for various clinical problems in treatment of SSc.

The impact of slice-reduced computed tomography on histogram-based densitometry assessment of lung fibrosis in patients with systemic sclerosis

Background

To evaluate usability of slice-reduced sequential computed tomography (CT) compared to standard high-resolution CT (HRCT) in patients with systemic sclerosis (SSc) for qualitative and quantitative assessment of interstitial lung disease (ILD) with respect to (I) detection of lung parenchymal abnormalities, (II) qualitative and semiquantitative visual assessment, (III) quantification of ILD by histograms and (IV) accuracy for the 20%-cut off discrimination.

Methods

From standard chest HRCT of 60 SSc patients sequential 9-slice-computed tomography (reduced HRCT) was retrospectively reconstructed. ILD was assessed by visual scoring and quantitative histogram parameters. Results from standard and reduced HRCT were compared using non-parametric tests and analysed by univariate linear regression analyses.

Results

With respect to the detection of parenchymal abnormalities, only the detection of intrapulmonary bronchiectasis was significantly lower in reduced HRCT compared to standard HRCT (P=0.039). No differences were found comparing visual scores for fibrosis severity and extension from standard and reduced HRCT (P=0.051-0.073). All scores correlated significantly (P<0.001) to histogram parameters derived from both, standard and reduced HRCT. Significant higher values of kurtosis and skewness for reduced HRCT were found (both P<0.001). In contrast to standard HRCT histogram parameters from reduced HRCT showed significant discrimination at cut-off 20% fibrosis (sensitivity 88% kurtosis and skewness; specificity 81% kurtosis and 86% skewness; cut-off kurtosis ≤26, cut-off skewness ≤4; both P<0.001).

Conclusions

Reduced HRCT is a robust method to assess lung fibrosis in SSc with minimal radiation dose with no difference in scoring assessment of lung fibrosis severity and extension in comparison to standard HRCT. In contrast to standard HRCT histogram parameters derived from the approach of reduced HRCT could discriminate at a threshold of 20% lung fibrosis with high sensitivity and specificity. Hence it might be used to detect early disease progression of lung fibrosis in context of monitoring and treatment of SSc patients.

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.

Quantitative computed tomography detects interstitial lung diseases proven by biopsy

Background: The Quantitative chest CT (QCT) is emerging as a promising tool in the assessment of interstitial lung disease (ILD). However, the precise relationship between QCT parameters and the fibrosis detectable in lung tissue, remains to be established. Objectives: The aim of this study was to compare QCT and histopathological features in patients with ILD. Moreover we verified if the QCT assessment is similar in patients with or without a ILD diagnosis proven by a biopsy. Methods: Twenty patients affected by ILD who underwent a chest CT and, later, a lung biopsy, were enrolled. Patients were divided according to the histopathological findings (IPF vs sarcoidosis) in two groups (respectively bIPF and bSarc). Other 20 patients with a radiological diagnosis of IPF were included in a control group (rIPF). All CTs were post-processed with a free software (Horos) in order to obtain an ILD quantitative assessment. Results: There were no differences in terms of gender, smoking habit and spirometric values between patients' groups. rIPF subjects were older than the other: 70 vs 59 and 47 years (p<0.001). A different distribution of QCT parameters was observed between bIPF and bSarc (p<0.01) while it was comparable within bIPF and rIPF. Conclusions: QCT parameters were similar in subjects affected by the same type of ILD detected with biopsy and with CT alone. These findings make stronger the assumption that QCT can identify the presence of pulmonary fibrosis and, ultimately, that it can represent an useful and effective tool to assess ILD. (Sarcoidosis Vasc Diffuse Lung Dis 2018; 35: 16-20).

Lung densitometry: why, how and when

Lung densitometry assesses with computed tomography (CT) the X-ray attenuation of the pulmonary tissue which reflects both the degree of inflation and the structural lung abnormalities implying decreased attenuation, as in emphysema and cystic diseases, or increased attenuation, as in fibrosis. Five reasons justify replacement with lung densitometry of semi-quantitative visual scales used to measure extent and severity of diffuse lung diseases: (I) improved reproducibility; (II) complete vs. discrete assessment of the lung tissue; (III) shorter computation times; (IV) better correlation with pathology quantification of pulmonary emphysema; (V) better or equal correlation with pulmonary function tests (PFT). Commercially and open platform software are available for lung densitometry. It requires attention to technical and methodological issues including CT scanner calibration, radiation dose, and selection of thickness and filter to be applied to sections reconstructed from whole-lung CT acquisition. Critical is also the lung volume reached by the subject at scanning that can be measured in post-processing and represent valuable information per se. The measurements of lung density include mean and standard deviation, relative area (RA) at -970, -960 or -950 Hounsfield units (HU) and 1st and 15th percentile for emphysema in inspiratory scans, and RA at -856 HU for air trapping in expiratory scans. Kurtosis and skewness are used for evaluating pulmonary fibrosis in inspiratory scans. The main indication for lung densitometry is assessment of emphysema component in the single patient with chronic obstructive pulmonary diseases (COPD). Additional emerging applications include the evaluation of air trapping in COPD patients and in subjects at risk of emphysema and the staging in patients with lymphangioleiomyomatosis (LAM) and with pulmonary fibrosis. It has also been applied to assess prevalence of smoking-related emphysema and to monitor progression of smoking-related emphysema, alpha1 antitrypsin deficiency emphysema, and pulmonary fibrosis. Finally, it is recommended as end-point in pharmacological trials of emphysema and lung fibrosis.

Assessment of lung involvement in sarcoidosis - the use of an open-source software to quantify data from computed tomography

Computed tomography (CT) plays a pivotal role in the initial evaluation of patients suspected of sarcoidosis. Although it has significant limitations associated with radiation exposure, CT scanning is also occasionally used to follow-up patients with sarcoidosis. Hitherto, no widely accepted method of quantitative assessment of pulmonary involvement in sarcoidosis has been established. The aims of the study were as follows: (1) to assess the utility of the open-source, free of charge DICOM Viewer software in quantitative analysis of pulmonary involvement in sarcoidosis; (2) to compare the parameters of quantitative CT analysis with the results of pulmonary function tests (PFTs). We included contrast-enhanced thorax CT examinations of 80 patients with sarcoidosis. Post-processing analysis of CT data was carried out using OsiriX Lite software (Pixmeo, Switzerland). Following densitometric parameters were measured: CT-derived lung volume (CT-LV), mean lung attenuation (MLA), kurtosis, skewness and standard deviation of lung radiodensity (SD_LR). Kurtosis was significantly lower in patients with lung fibrosis comparing to those with mediastinal and/or hilar lymphadenopathy (MHL) and pulmonary involvement (median 1.49 vs 1.93). Furthermore, SD_LR was significantly higher in patients with lung fibrosis comparing to those with isolated MHL and MHL with pulmonary involvement (median 163.6 vs 137.4). Also, significant correlations between densitometric parameters and the results of PFTs were demonstrated, including correlation between CT-LV and TLC (R=0.7). Our study showed that post-processing of the CT data with the use of Osirix Lite DICOM Viewer might be a valuable method of quantitative analysis of pulmonary involvement in sarcoidosis. (Sarcoidosis Vasc Diffuse Lung Dis 2017; 34: 315-325).

Interstitial lung disease in systemic sclerosis: where do we stand?

Interstitial lung disease (ILD) is common in systemic sclerosis (SSc) patients and despite recent advances in the treatment is, at present, the major cause of death. Today, an early diagnosis of ILD is possible, and is mandatory to improve the prognosis of the disease. Pulmonary function tests and high-resolution computed tomography remain the mainstay for the diagnosis of SSc-ILD, but there is a growing interest in lung ultrasound. Recently, the correlation between severity of fibrosis and some peripheral blood biomarkers has been described. Nonselective immunosuppressors are still the main treatment for ILD, with cyclophosphamide (CYC) most widely used to obtain remission. Novel therapies towards specific molecular and cellular targets have been suggested; in particular, rituximab (RTX) has shown promising results, but further research is needed. It is of paramount importance to define the severity of the disease and the risk of progression in order to define the need for treatment and the treatment intensity. We propose the division of the treatment strategies at our disposal to induce remission into three categories: high intensity (haematopoietic stem cell transplantation), medium intensity (CYC and RTX) and low intensity (azathioprine (AZA) and mycophenolate mofetil (MMF)). After obtaining remission, maintenance treatment with AZA or MMF should be started. In this review we explore new advances in the pathogenesis, diagnosis and treatment of SSc-ILD.

Computer-Aided Tomographic Analysis of Interstitial Lung Disease (ILD) in Patients with Systemic Sclerosis (SSc). Correlation with Pulmonary Physiologic Tests and Patient-Centred Measures of Perceived Dyspnea and Functional Disability

Objectives

This study was designed (a) to evaluate an improved quantitative lung fibrosis score based on a computer-aided diagnosis (CaM) system in patients with systemic sclerosis (SSc),—related interstitial lung disease (SSc-ILD), (b) to investigate the relationship between physiologic parameters (forced vital capacity [FVC] and single-breath diffusing capacity for carbon monoxide [DLCO]), patient-centred measures of dyspnea and functional disability and CaM and visual reader-based (CoVR) methods, and (c) to identify potential surrogate measures from quantitative and visual HRCT measurement.

Methods

126 patients with SSc underwent chest radiography, HRCT and PFTs. The following patient-centred measures were obtained: modified Borg Dyspnea Index (Borg score), VAS for breathing, and Health Assessment Questionnaire-Disability Index (HAQ-DI). HRCT abnormalities were scored according to the conventional visual reader-based score (CoVR) and by a CaM. The relationships among the HRCT scores, physiologic parameters (FVC and DLCO, % predicted) results and patient-centred measures, were calculated using linear regression analysis and Pearson’s correlation. Multivariate regression models were performed to identify the predictor variables on severity of pulmonary fibrosis.

Results

Subjects with limited cutaneous SSc had lower HAQ-DI scores than subjects with diffuse cutaneous SSc (p <0.001). CaM and CoVR scores were similar in the 2 groups. In univariate analysis, a strong correlation between CaM and CoVR was observed (p <0.0001). In multivariate analysis the CaM and CoVR scores were predicted by DLco, FVC, Borg score and HAQ-DI. Age, sex, disease duration, anti-topoisomerase antibodies and mRSS were not significantly associated with severity of pulmonary fibrosis on CaM- and CoVR methods.

Conclusions

Although a close correlation between CaM score results and CoVR total score was found, CaM analysis showed a more significant correlation with DLco (more so than the FVC), patient-centred measures of perceived dyspnea and functional disability. Computer-aided tomographic analysis is computationally efficient, and in combination with physiologic and patient-centred measures, it could allow a means for accurately assessing and monitoring the disease progression or response to therapy.

Quantitative diagnosis of connective tissue disease-associated interstitial pneumonia using thoracic computed tomography images

Patients with polymyositis (PM) or dermatomyositis (DM) frequently show interstitial pneumonia (IP), which is sometimes rapidly progressive or resistant to treatment, thereby significantly affecting the prognosis. The diagnosis and response evaluation of IP are commonly performed qualitatively based on imaging findings, which may cause disagreement among rheumatologists in the evaluation of early lesions and atypical interstitial changes. To determine whether IP could be diagnosed in a quantitative manner during the early stage of PM/DM using a workstation that allows quantitative image processing. Thoracic computed tomography (CT) images of 20 PM/DM patients were reconstructed into a three-dimensional (3D) image using an image processing workstation. The CT values of the constituent voxels were arranged in a histogram of -1000 to +1000 Hounsfield units (HU). The most frequent lung field density was -900 to -801 HU, and relative size was as follows: IP (+) group 0.45 and IP (-) group 0.53. Between -1000 and -701 HU, relative size was not significantly different between the IP (+) group and IP (-) group. Between -700 and -1 HU, the relative size of the lung field was significantly larger in the IP (+) than in the IP (-) group, demonstrating its IP-diagnosing ability. Particularly, within the range from -700 to -301 HU, the macroscopically-assessed ground glass opacity was consistent with the CT value, which, in turn, was closely correlated with KL-6, the pre-existing marker for IP diagnosis. The results of this study may lead to the establishment of quantitative methods of evaluating IP and possible elucidation of the pathogenesis of IP.

Clinical impact of high-attenuation and cystic areas on computed tomography in fibrotic idiopathic interstitial pneumonias

Background

Quantitative computed tomography (CT) analysis has been proposed as a means of objectively assessing fibrotic interstitial pneumonia (IP) including idiopathic pulmonary fibrosis (IPF). We investigated whether percentages of high-attenuation areas (HAA%) and cystic areas (CA%) quantified from CT images were useful as indices of fibrotic IP.

Methods

CT images of 74 patients with fibrotic idiopathic interstitial pneumonias (IPF, 36; non-specific interstitial pneumonia, 9; unclassifiable idiopathic interstitial pneumonia, 29) were analyzed via in-house computer software, which automatically calculated HAA%, CA%, mean lung density (MLD), standard deviation of lung density (SD-LD), kurtosis, and skewness from CT attenuation histograms. These indices were compared in each instance with physiologic measures, visual fibrosis score, clinical diagnosis, radiologic CT pattern, and prognosis.

Results

HAA% correlated significantly with physiologic measures and visual fibrosis score to a moderate extent (%forced vital capacity, r_s = −0.59; % carbon monoxide diffusion capacity, r_s = −0.43; fibrosis score, r_s = 0.23). Densitometric parameters (MLD, SD-LD, kurtosis, and skewness) correlated significantly with physiologic measures and fibrosis score (|r_s| = 0.28-0.59). CA% showed no association with pulmonary functions but differed significantly between IPF and other interstitial pneumonias (IPs) (1.50 ± 2.41 % vs. 0.41 ± 0.80 %; P < 0.01) and between the definite usual interstitial pneumonia (UIP) pattern and other patterns (1.48 ± 2.38 % vs. 0.55 ± 1.19 %; P < 0.01). On univariate analysis, HAA%, MLD, SD-LD, kurtosis, skewness, fibrosis score, and definite UIP pattern all correlated with survival, with kurtosis alone identified as a significant predictor of mortality on multivariate analysis (hazard ratio = 0.67; 95 % CI, 0.44-0.96; P = 0.03).

Conclusion

CA% and HAA% are novel quantitative CT indices with differing properties in fibrotic IP evaluations. HAA% largely reflects physiologic impairments, whereas CA% corresponds with diagnosis and HRCT pattern. Of the CT indices examined, kurtosis constituted the strongest predictor of mortality.

Connective Tissue Disease-related Thoracic Disease

Pulmonary involvement is a frequent manifestation of connective tissue disease (CTD)-related thoracic disease. It is important to characterize the underlying pattern when pulmonary involvement occurs in a patient with CTD, and to exclude other causes. A systematic approach, evaluating each compartment of the lung (airway, interstitium, pleura, pulmonary vasculature) may be helpful. In complex cases, a multidisciplinary approach should be considered, potentially including the pulmonologist, rheumatologist, radiologist, pathologist, and sometimes the infectious disease specialist or oncologist. New techniques, such as quantitative computed tomography and MRI, are expected to be helpful for evaluation and management of CTD-associated thoracic disease.

CT density distribution analysis in patients with cystic fibrosis: correlation with pulmonary function and radiologic scores

RATIONALE AND OBJECTIVES

The progressive changes in lung morphology observed in cystic fibrosis (CF) can potentially affect the statistical distribution of computed tomography (CT) density values. This study aimed to characterize the lung CT density distributions by quantifying indices of the kurtosis and skewness of the lung density distribution and to compare these indices to radiologic scores and lung function parameters in children and young adults with CF.

MATERIALS AND METHODS

CT scans and lung function of 26 patients with CF were retrospectively examined. The Bhalla radiologic scoring was performed separately, in random order, by two expert radiologists, blinded to the patient's identity, age, clinical status, results of lung function tests, and the other paired observer's score.

RESULTS

Positive relations were evidenced between the log indices of lung density distribution kurtosis (iKurtosis) and the overall radiologic scores (RS) of both observers (R = 0.58; P < .001 vs RS1 and R = 0.71; P < .001 vs RS2). A similar relationship was evidenced with the log index of the degree of distribution asymmetry (iSkewness; R = 0.62; P < .001 vs RS1 and R = 0.62; P < .001 vs RS2). Log-iKurtosis and log-iSkewness were related to FEV1 (R = -0.56; P < 10(-5) and R = -0.55; P < 10(-5)) and to residual volume (R = 0.40; P < .001 and R = 0.45; P < .001, respectively). Both radiologic scores showed significant relation with lung function. The correlation between RS1 and RS2 was excellent (R = 0.93), with a Cohen weighted kappa of 0.43.

CONCLUSIONS

Characteristic indices of lung CT density distribution are correlated to lung function and radiologic scores in patients with CF and merit further evaluation as part of more comprehensive automated methods for quantifying CF lung CT data.