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

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy

BACKGROUND AND PURPOSE

Symptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP.

MATERIALS AND METHODS

AIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT's quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort.

RESULTS

Overall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively.

CONCLUSION

AIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning

RATIONALE AND OBJECTIVES

The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features.

MATERIALS AND METHODS

A total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n = 274 with normal lung function (stable control), n = 22 stable PRISm, and n = 300 stable COPD. Investigated features included: quantitative CT (QCT) features (n = 34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n = 102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values.

RESULTS

Machine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p < 0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV.

CONCLUSION

These findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.

Histogram-based analysis in progressive pulmonary fibrosis: relationships between pulmonary functional tests and HRCT indexes

OBJECTIVES

To investigate relationships between histogram-based high-resolution CT (HRCT) indexes and pulmonary function tests (PFTs) in interstitial lung diseases.

METHODS

Forty-nine patients having baseline and 1-year HRCT examinations and PFTs were investigated. Histogram-based HRCT indexes were calculated; strength of associations with PFTs was investigated using Pearson correlation. Patients were divided into progressive and non-progressive groups. HRCT indexes were compared between the two groups using the U-test; within each group, baseline and follow-up Wilcoxon analysis was performed. Receiver operating characteristic analysis was used for predicting disease progression.

RESULTS

At baseline, moderate correlations were observed considering kurtosis and diffusion capacity of the lungs for carbon monoxide (DLCO) (r = 0.54) and skewness and DLCO (r = 0.559), whereas weak but significant correlations were observed between forced vital capacity and kurtosis (r = 0.368, p = 0.009) and forced vital capacity and skewness (r = 0.391, p = 0.005). Negative correlations were reported between HAA% and PFTs (from r = -0.418 up to r = -0.507). At follow-up correlations between quantitative indexes and PFTs were also moderate, except for high attenuation area (HAA)% -700 and DLCO (r = -0.397). In progressive subgroup, moderate and strong correlations were found between DLCO and HRCT indexes (r = 0.595 kurtosis, r = 0.672 skewness, r=-0. 598 HAA% -600 and r = -0.626 HAA% -700). At follow-up, we observed significant differences between the two groups for kurtosis (p = 0.029), HAA% -600 (p = 0.04) and HAA% -700 (p = 0.02). To predict progression, ROC analysis reported sensitivity of 90.9% and specificity of 51.9% using a threshold value of δ kurtosis <0.03.

CONCLUSION

At one year, moderate correlations suggest that progression could be assessed through HRCT quantification.

ADVANCES IN KNOWLEDGE

This study promotes histogram-based HRCT indexes in the assessment of progressive pulmonary fibrosis.

Quantitative Assessment and Comparative Analysis of Longitudinal Lung CT Scans of Chest-Irradiated Nonhuman Primates

Computed tomography (CT) imaging has been used to diagnose radiation-induced lung injury for decades. However, histogram-based quantitative tools have rarely been applied to assess lung abnormality due to radiation-induced lung injury (RILI). Here, we used first-order summary statistics to derive and assess threshold measures extracted from whole lung histograms of CT radiodensity in rhesus macaques. For the present study, CT scans of animals exposed to 10 Gy of whole thorax irradiation were utilized from a previous study spanning 2-9 months postirradiation. These animals were grouped into survivors and non-survivors based on their clinical and experimental endpoints. We quantified the change in lung attenuation after irradiation relative to baseline using three density parameters; average lung density (ALD), percent change in hyper-dense lung volume (PCHV), hyperdense volume as a percent of total volume (PCHV/TV) at 2-month intervals and compared each parameter between the two irradiated groups (non-survivors and survivors). We also correlated our results with histological findings. All the three indices (ALD, PCHV, PCHV/TV) obtained from density histograms showed a significant increase in lung injury in non-survivors relative to survivors, with PCHV relatively more sensitive to detect early RILI changes. We observed a significant positive correlation between histologic pneumonitis scores and each of the three CT measurements, indicating that CT density is useful as a surrogate for histologic disease severity in RILI. CT-based three density parameters, ALD, PCHV, PCHV/TV, may serve as surrogates for likely histopathology patterns in future studies of RILI disease progression.

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.

Novel Artificial Intelligence-based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis

RATIONALE

There is a growing need to accurately estimate the prognosis of idiopathic pulmonary fibrosis (IPF) in clinical practice, given the development of effective drugs for treating IPF.

OBJECTIVE

To develop artificial intelligence-based image analysis software to detect parenchymal and airway abnormalities on chest computed tomography (CT) and to explore their prognostic importance in patients with IPF.

METHODS

A novel artificial intelligence-based quantitative CT image analysis software (AIQCT) was developed by applying 304 HRCT scans from patients with diffuse lung diseases as the training set. AIQCT automatically categorized and quantified ten types of parenchymal patterns as well as airways, expressing the volumes as percentages of the total lung volume. To validate the software, the area percentages of each lesion quantified by AIQCT were compared with those of the visual scores using 30 plain HRCT images with lung diseases. In addition, three-dimensional analysis for similarity with ground truth was performed using HRCT images from 10　patients with IPF. AIQCT was then applied to 120 patients with IPF who underwent chest HRCT scanning at our institute. Associations between the measured volumes and survival were analyzed.

RESULTS

The correlations between AIQCT and the visual scores were moderate to strong (correlation coefficient 0.44 to 0.95) depending on the parenchymal pattern. The Dice indexes for similarity between AIQCT data and ground truth were 0.67, 0.76, and 0.64 for reticulation, honeycomb, and bronchi, respectively. During a median follow-up period of 2,184 days, 66 patients died, and 1 underwent lung transplantation. In multivariable Cox regression analysis, bronchial volumes [adjusted hazard ratio (HR), 1.33; 95% confidence interval (CI), 1.16 to 1.53] and normal lung volumes (adjusted HR, 0.97; 95% CI, 0.94 to 0.99) were independently associated with survival after adjusting for the GAP stage of IPF.

CONCLUSIONS

Our newly developed artificial intelligence-based image analysis software successfully quantified parenchymal lesions and airway volumes. Bronchial and normal lung volumes on chest HRCT may provide additional prognostic information on the GAP stage of IPF.

Small Airway Reduction and Fibrosis is an Early Pathologic Feature of Idiopathic Pulmonary Fibrosis

RATIONALE

To improve disease outcomes in idiopathic pulmonary fibrosis (IPF) it is essential to understand its early pathophysiology so that it can be targeted therapeutically.

OBJECTIVES

Perform three-dimensional (3D) assessment of the IPF lung micro-structure using stereology and multi-resolution computed tomography (CT) imaging.

METHODS

Explanted lungs from IPF patients (n=8) and donor controls (n=8) were inflated with air and frozen. CT scans were used to assess large airways. Unbiased, systematic uniform random (SUR) samples (n=8/lung) were scanned with microCT for stereological assessment of small airways (number, airway wall and lumen area) and parenchymal fibrosis (volume fraction of tissue, alveolar surface area, and septal wall thickness).

RESULTS

The total number of airways on clinical CT was greater in IPF lungs than control lungs (p<0.01), due to an increase in the wall (p<0.05) and lumen area (p<0.05) resulting in more visible airways with a lumen larger than 2 mm. In IPF tissue samples without microscopic fibrosis, assessed by the volume fraction of tissue using microCT, there was a reduction in the number of the terminal (p<0.01) and transitional (p<0.001) bronchioles, and an increase in terminal bronchiole wall area (p<0.001) compared to control lungs. In IPF tissue samples with microscopic parenchymal fibrosis, terminal bronchioles had increased airway wall thickness (p<0.05), and dilated airway lumens (p<0.001) leading to honeycomb cyst formations.

CONCLUSION

This study has important implications for the current thinking on how the lung tissue is remodeled in IPF, and highlights small airways as a potential target to modify IPF outcomes.

A homological approach to a mathematical definition of pulmonary fibrosis and emphysema on computed tomography

Three-dimensional imaging is essential to evaluate local abnormalities and understand structure-function relationships in an organ. However, quantifiable and interpretable methods to localize abnormalities remain unestablished. Visual assessments are prone to bias, machine learning methods depend on training images, and the underlying decision principle is usually difficult to interpret. Here, we developed a homological approach to mathematically define emphysema and fibrosis in the lungs on computed tomography (CT). With the use of persistent homology, the density of homological features, including connected components, tunnels, and voids, was extracted from the volumetric CT scans of lung diseases. A pair of CT values at which each homological feature appeared (birth) and disappeared (death) was computed by sweeping the threshold levels from higher to lower CT values. Consequently, fibrosis and emphysema were defined as voxels with dense voids having a longer lifetime (birth-death difference) and voxels with dense connected components having a lower birth, respectively. In an independent dataset including subjects with idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), and combined pulmonary fibrosis and emphysema (CPFE), the proposed definition enabled accurate segmentation with comparable quality to deep learning in terms of Dice coefficients. Persistent homology-defined fibrosis was closely associated with physiological abnormalities such as impaired diffusion capacity and long-term mortality in subjects with IPF and CPFE, and persistent homology-defined emphysema was associated with impaired diffusion capacity in subjects with COPD. The present persistent homology-based evaluation of structural abnormalities could help explore the clinical and physiological impacts of structural changes and morphological mechanisms of disease progression.NEW & NOTEWORTHY This study proposes a homological approach to mathematically define a three-dimensional texture feature of emphysema and fibrosis on chest computed tomography using persistent homology. The proposed definition enabled accurate segmentation with comparable quality to deep learning while offering higher interpretability than deep learning-based methods.

The qualitative and quantitative high-resolution computed tomography in the evaluation of interstitial lung diseases

Background

High-resolution computed tomography (HRCT) is the most accepted imaging tool for the detection, characterization, and monitoring of interstitial lung diseases (ILDs). The correct interpretation of HRCT findings still represents often a problem for the radiologists since there is wide interobserver variability. Therefore, a quantitative and noninvasive imaging method able to permit an accurate assessment of ILD is highly desirable. The purpose of this study is to compare the visual method and quantitative CT histogram in the evaluation of ILDs and to identify the best quantitative parameter in the prediction of severity of ILDs.

Results

There is a correlation between the HRCT score by the qualitative method and CT histogram parameters by the quantitative method in the evaluation of ILDs. Total lung volume inspiratory, mean lung density expiratory, and high attenuation area expiratory showed a significant correlation with the HRCT score.

Conclusion

The single best predictor of fibrosis severity in interstitial lung disease is HAAs % expiratory.

Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT

BACKGROUND

Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests.

METHODS

We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely -200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood.

RESULTS

Our Poisson models reveal no difference at the -200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at -200 HU shows the most significant diagnostic utility.

CONCLUSIONS

The percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.

Assessment of Lung Cancer Development in Idiopathic Pulmonary Fibrosis Patients Using Quantitative High-Resolution Computed Tomography: A Retrospective Analysis

PURPOSE

The purpose of this study was to investigate histogram-based quantitative high-resolution computed tomography (HRCT) indexes in the assessment of lung cancer (LC) development in idiopathic pulmonary fibrosis (IPF) patients.

MATERIALS AND METHODS

From IPF databases of 2 national respiratory centers, we retrospectively studied patients with and without LC development-respectively, divided into Group A (n=16) and Group B (n=33). The extent of fibrotic disease was quantified on baseline and follow-up HRCT examinations using kurtosis, skewness, percentage of high attenuation area (HAA%), and percentage of fibrotic area (FA%). These indexes were compared between the 2 groups using the Mann-Whitney U test. In the prediction of LC development, receiver operating characteristic analysis was performed to assess threshold values of HRCT indexes.

RESULTS

At baseline, no difference was reported among groups for kurtosis, skewness, HAA%, and FA%, with P-values of 0.0881, 0.0606, 0.0578, and 0.0606, respectively. On follow-up, significant differences were reported, with P-values of 0.0174 for kurtosis, 0.0313 for skewness, 0.0297 for HAA%, and 0.0407 for FA%.On baseline HRCT, in the prediction of LC development, receiver operating characteristic analysis reported sensibility and specificity of 87.5% and 45.45% for kurtosis, 68.75% and 63.64% for skewness, 81.25% and 54.55% for FA%, and 75% and 60.61% for HAA%.

CONCLUSIONS

LC development is associated with progression of fibrosis; at baseline, FA% and HAA% reported more convenient sensitivity/specificity ratios in the prediction of LC development.

Quantitative CT Analysis of Diffuse Lung Disease

Quantitative analysis of thin-section CT of the chest has a growing role in the clinical evaluation and management of diffuse lung diseases. This heterogeneous group includes diseases with markedly different prognoses and treatment options. Quantitative tools can assist in both accurate diagnosis and longitudinal management by improving characterization and quantification of disease and increasing the reproducibility of disease severity assessment. Furthermore, a quantitative index of disease severity may serve as a useful tool or surrogate endpoint in evaluating treatment efficacy. The authors explore the role of quantitative imaging tools in the evaluation and management of diffuse lung diseases. Lung parenchymal features can be classified with threshold, histogram, morphologic, and texture-analysis-based methods. Quantitative CT analysis has been applied in obstructive, infiltrative, and restrictive pulmonary diseases including emphysema, cystic fibrosis, asthma, idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue-related interstitial lung disease, and combined pulmonary fibrosis and emphysema. Some challenges limiting the development and practical application of current quantitative analysis tools include the quality of training data, lack of standard criteria to validate the accuracy of the results, and lack of real-world assessments of the impact on outcomes. Artifacts such as patient motion or metallic beam hardening, variation in inspiratory effort, differences in image acquisition and reconstruction techniques, or inaccurate preprocessing steps such as segmentation of anatomic structures may lead to inaccurate classification. Despite these challenges, as new techniques emerge, quantitative analysis is developing into a viable tool to supplement the traditional visual assessment of diffuse lung diseases and to provide decision support regarding diagnosis, prognosis, and longitudinal evaluation of disease. ^©RSNA, 2019.

Heterogeneity in Unclassifiable Interstitial Lung Disease. A Systematic Review and Meta-Analysis

RATIONALE

Accurate diagnosis of interstitial lung disease is necessary to identify the most appropriate management strategy and to inform prognosis. Many patients cannot be provided a confident diagnosis, despite an exhaustive search for potential etiologies and review in a multidisciplinary conference, and are consequently labeled with unclassifiable interstitial lung disease.

OBJECTIVES

To systematically review and meta-analyze previous studies reporting on the diagnostic criteria, prevalence, clinical features, and outcome of unclassifiable interstitial lung disease.

METHODS

MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials databases were systematically searched for all studies related to unclassifiable interstitial lung disease published before September 1, 2017. Two authors independently screened each citation for eligibility criteria, serially reviewing the title, abstract, and full-text manuscript, and then abstracted data pertaining to the study objectives from eligible studies. Articles were stratified by risk of selection bias, whether the publication stated that patients were reviewed in a multidisciplinary discussion, and by the frequency of surgical lung biopsy. Meta-analyses and meta-regression were performed to calculate the pooled prevalence of unclassifiable interstitial lung disease within an interstitial lung disease population and within specific subgroups to identify reasons for across-study heterogeneity.

RESULTS

The search identified 10,130 unique citations, 313 articles underwent full-text review, and eligibility criteria were met in 88 articles. Twenty-two studies were deemed low risk of selection bias, including 1,060 patients with unclassifiable interstitial lung disease from a total of 10,174 patients with interstitial lung disease. The terminology and definition of unclassifiable interstitial lung disease varied substantially across publications, with inconsistent diagnostic criteria and evaluation processes. The prevalence of unclassifiable interstitial lung disease was 11.9% (95% confidence interval, 8.5-15.6%), with lower prevalence in centers that reported use of a formal multidisciplinary discussion of cases (9.5% vs. 14.5%). Four articles reported survival of unclassifiable interstitial lung disease, with 1-year, 2-year, and 5-year survival of 84% to 89%, 70% to 76%, and 46% to 70%, respectively.

CONCLUSIONS

This systematic review and meta-analysis shows that unclassifiable interstitial lung disease is common but has substantial heterogeneity and inconsistent definitions across interstitial lung disease cohorts. These findings highlight important limitations in multicenter studies of fibrotic interstitial lung disease and the need for a standardized approach to interstitial lung disease diagnostic classification.

Predicting Outcome in Idiopathic Pulmonary Fibrosis: Addition of Fibrotic Score at Thin-Section CT of the Chest to Gender, Age, and Physiology Score Improves the Prediction Model

Purpose

To assess the impact of adding thin-section CT-derived semiquantitative fibrotic score to gender, age, and physiology (GAP) model for predicting survival in idiopathic pulmonary fibrosis (IPF).

Materials and Methods

In this retrospective study of 194 patients with IPF, primary outcome was transplant-free survival. Two thoracic radiologists visually estimated the percentage of reticulation and honeycombing at baseline thin-section CT, which were added to give fibrotic score. For analysis, fibrotic score cutoff (x) determined by using receiver operating characteristic analysis categorized patients into group A (<x) and group B (≥x). Another categorization based on GAP score created group 1 (score 0-3) and group 2 (score >3). Combining the above categories gave four groups (A1, A2, B1, B2). Kaplan-Meier survival analysis was performed with comparison statistics (log-rank test), and hazard ratios were calculated by using the Cox model.

Results

The study patients included 141 men (72.7%), with average age of 66.1 years ± 9.1 (standard deviation). Eighty-four patients (43.3%) has stage I disease with a median follow up of 3.3 years. The interobserver agreement for thin-section CT fibrotic score was substantial (83.3%; κ = 0.64). The optimal cutoff for fibrotic score was 25% (x), with area under the curve of 0.654 (95% confidence interval [CI]: 0.569, 0.74). Survival for group A1 was significantly better than in the other three groups (P < .001). The hazard ratios for respective groups were as follows: B1 was 4.03 (95% CI: 2.02, 8.07), A2 was 4.10 (95% CI: 1.89, 8.87), and B2 was 5.62 (95% CI: 2.86, 11.06) (P < .001 for all). Within the group with GAP score less than or equal to 3 (A1, B1), participants with higher fibrotic score (B1) had four times the increased risk of death or transplantation (P < .001).

Conclusion

Incorporating semiquantitative fibrotic score from thin-section CT to GAP score provides an improved prediction model for survival in idiopathic pulmonary fibrosis.© RSNA, 2019See also the commentary by Chung in this issue.

Experimental and quantitative imaging techniques in interstitial lung disease

Interstitial lung diseases (ILDs) are a heterogeneous group of conditions, with a wide and complex variety of imaging features. Difficulty in monitoring, treating and exploring novel therapies for these conditions is in part due to the lack of robust, readily available biomarkers. Radiological studies are vital in the assessment and follow-up of ILD, but currently CT analysis in clinical practice is qualitative and therefore somewhat subjective. In this article, we report on the role of novel and quantitative imaging techniques across a range of imaging modalities in ILD and consider how they may be applied in the assessment and understanding of ILD. We critically appraised evidence found from searches of Ovid online, PubMed and the TRIP database for novel and quantitative imaging studies in ILD. Recent studies have explored the capability of texture-based lung parenchymal analysis in accurately quantifying several ILD features. Newer techniques are helping to overcome the challenges inherent to such approaches, in particular distinguishing peripheral reticulation of lung parenchyma from pleura and accurately identifying the complex density patterns that accompany honeycombing. Robust and validated texture-based analysis may remove the subjectivity that is inherent to qualitative reporting and allow greater objective measurements of change over time. In addition to lung parenchymal feature quantification, pulmonary vessel volume analysis on CT has demonstrated prognostic value in two retrospective analyses and may be a sign of vascular changes in ILD which, to date, have been difficult to quantify in the absence of overt pulmonary hypertension. Novel applications of existing imaging techniques, such as hyperpolarised gas MRI and positron emission tomography (PET), show promise in combining structural and functional information. Although structural imaging of lung tissue is inherently challenging in terms of conventional proton MRI techniques, inroads are being made with ultrashort echo time, and dynamic contrast-enhanced MRI may be used for lung perfusion assessment. In addition, inhaled hyperpolarised ¹²⁹Xenon gas MRI may provide multifunctional imaging metrics, including assessment of ventilation, intra-acinar gas diffusion and alveolar-capillary diffusion. PET has demonstrated high standard uptake values (SUVs) of ¹⁸F-fluorodeoxyglucose in fibrosed lung tissue, challenging the assumption that these are ‘burned out’ and metabolically inactive regions. Regions that appear structurally normal also appear to have higher SUV, warranting further exploration with future longitudinal studies to assess if this precedes future regions of macroscopic structural change. Given the subtleties involved in diagnosing, assessing and predicting future deterioration in many forms of ILD, multimodal quantitative lung structure-function imaging may provide the means of identifying novel, sensitive and clinically applicable imaging markers of disease. Such imaging metrics may provide mechanistic and phenotypic information that can help direct appropriate personalised therapy, can be used to predict outcomes and could potentially be more sensitive and specific than global pulmonary function testing. Quantitative assessment may objectively assess subtle change in character or extent of disease that can assist in efficacy of antifibrotic therapy or detecting early changes of potentially pneumotoxic drugs involved in early intervention studies.

Computer-aided identification of interstitial lung disease based on computed tomography

OBJECTIVE

Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls.

MATERIALS AND METHODS

We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers.

RESULTS

Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05).

CONCLUSIONS

This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs.

Assessment of survival in patients with idiopathic pulmonary fibrosis using quantitative HRCT indexes

Background

The assessment of Idiopathic Pulmonary Fibrosis (IPF) using HRCT requires great experience and is limited by a significant inter-observer variability, even between trained radiologists. The evaluation of HRCT through automated quantitative analysis may hopefully solve this problem. The accuracy of CT-histogram derived indexes in the assessment of survival in IPF patients has been poorly studied.

Methods

Forty-two patients with a diagnosis of IPF and a follow up time of 3 years were retrospectively collected; HRCT and Pulmonary Function Tests (PFTs) performed at diagnosis time were analysed; the extent of fibrotic disease was quantified on HRCT using kurtosis, skewness, Mean Lung Density (MLD), High attenuation areas (HAA%) and Fibrotic Areas (FA%). Univariate Cox regression was performed to assess hazard ratios for the explored variables and a multivariate model considering skewness, FVC, DL_CO and age was created to test their prognostic value in assessing survival. Through ROC analysis, threshold values demonstrating the best sensitivity and specificity in predicting mortality were identified. They were used as cut-off points to graph Kaplan-Meier curves specific for the CT-indexes.

Results

Kurtosis, skewness, MLD, HAA% and FA% were good predictors of mortality (HR 0.44, 0.74, 1.01, 1.12, 1.06; p = 0.03, p = 0.01, p = 0.02, p = 0.02 and p = 0.017 respectively). Skewness demonstrated the lowest Akaike's information criterion value (55.52), proving to be the best CT variable for prediction of mortality. Significant survival differences considering proposed cut-off points were also demonstrated according to kurtosis (p = 0.02), skewness (p = 0.005), MLD (p = 0.003), HAA% (p = 0.009) and FA% (p = 0.02) - obtained from quantitative HRCT analysis at diagnosis time.

Conclusions

CT-histogram derived indexes may provide an accurate estimation of survival in IPF patients. They demonstrate a correlation with PFTs, highlighting their possible use in clinical practice.

Collagen biomarkers and subclinical interstitial lung disease: The Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Lung fibrosis is attributed to derangements in extracellular matrix remodeling, a process driven by collagen turnover. We examined the association of two collagen biomarkers, carboxy-terminal telopeptide of collagen type I (ICTP) and amino-terminal propeptide of type III procollagen (PIIINP), with subclinical interstitial lung disease (ILD) in adults.

METHODS

We performed a cross-sectional analysis of 3244 participants age 45-84 years in the Multi-Ethnic Study of Atherosclerosis. Serum ICTP and PIIINP levels were measured at baseline by radioimmunoassay. Subclinical ILD was defined as high attenuation areas (HAA) in the lung fields on baseline cardiac CT scans. Interstitial lung abnormalities (ILA) were measured in 1082 full-lung CT scans at 9.5 years median follow-up. We used generalized linear models to examine the associations of collagen biomarkers with HAA and ILA.

RESULTS

Median (IQR) for ICTP was 3.2 μg/L (2.6-3.9 μg/L) and for PIIINP was 5.3 μg/L (4.5-6.2 μg/L). In fully adjusted models, each SD increment in ICTP was associated with a 1.3% increment in HAA (95% CI 0.2-2.4%, p = 0.02) and each SD increment in PIIINP was associated with a 0.96% increment in HAA (95% CI 0.06-1.9%, p = 0.04). There was no association between ICTP or PIIINP and ILA. There was no evidence of effect modification by gender, race, smoking status or eGFR.

CONCLUSIONS

Higher levels of collagen biomarkers are associated with greater HAA independent of gender, race and smoking status. This suggests that extracellular matrix remodeling may accompany subclinical ILD prior to the onset of clinically evident disease.

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.

Functional and prognostic effects when emphysema complicates idiopathic pulmonary fibrosis

This study aimed to investigate whether the combination of fibrosis and emphysema has a greater effect than the sum of its parts on functional indices and outcome in idiopathic pulmonary fibrosis (IPF), using visual and computer-based (CALIPER) computed tomography (CT) analysis.Consecutive patients (n=272) with a multidisciplinary IPF diagnosis had the extent of interstitial lung disease (ILD) scored visually and by CALIPER. Visually scored emphysema was subcategorised as isolated or mixed with fibrotic lung. The CT scores were evaluated against functional indices forced vital capacity (FVC), diffusing capacity of the lungs for carbon monoxide (D_LCO), transfer coefficient of the lung for carbon monoxide (K_CO), composite physiologic index (CPI)) and mortality.The presence and extent of emphysema had no impact on survival. Results were maintained following correction for age, gender, smoking status and baseline severity using D_LCO, and combined visual emphysema and ILD extent. Visual emphysema quantitation indicated that relative preservation of lung volumes (FVC) resulted from tractionally dilated airways within fibrotic lung, ventilating areas of admixed emphysema (p<0.0001), with no independent effect on FVC from isolated emphysema. Conversely, only isolated emphysema (p<0.0001) reduced gas transfer (D_LCO).There is no prognostic impact of emphysema in IPF, beyond that explained by the additive extents of both fibrosis and emphysema. With respect to the location of pulmonary fibrosis, emphysema distribution determines the functional effects of emphysema.