Computer-based quantitative computed tomography image analysis in idiopathic pulmonary fibrosis: A mini review

A reference equation for lung volume on computed tomography in Japanese middle-aged and elderly adults

BACKGROUND

Effective use of lung volume data measured on computed tomography (CT) requires reference values for specific populations. This study examined whether an equation previously generated for multiple ethnic groups in the United States, including Asians predominantly composed of Chinese people, in the Multi-Ethnic Study of Atherosclerosis (MESA) could be used for Japanese people and, if necessary, to optimize this equation. Moreover, the equation was used to characterize patients with chronic obstructive pulmonary disease (COPD) and lung hyperexpansion.

METHODS

This study included a lung cancer screening CT cohort of asymptomatic never smokers aged ≥40 years from two institutions (n = 364 and 419) to validate and optimize the MESA equation and a COPD cohort (n = 199) to test its applicability.

RESULTS

In all asymptomatic never smokers, the variance explained by the predicted values (R2) based on the original MESA equation was 0.60. The original equation was optimized to minimize the root mean squared error (RMSE) by adjusting the scaling factor but not the age, sex, height, or body mass index terms of the equation. The RMSE changed from 714 ml in the original equation to 637 ml in the optimized equation. In the COPD cohort, lung hyperexpansion, defined based on the 95th percentile of the ratio of measured lung volume to predicted lung volume in never smokers (122 %), was observed in 60 (30 %) patients and was associated with centrilobular emphysema and air trapping on inspiratory/expiratory CT.

CONCLUSIONS

The MESA equation was optimized for Japanese middle-aged and elderly adults.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Tissue fibrosis induced by radiotherapy: current understanding of the molecular mechanisms, diagnosis and therapeutic advances

Cancer remains the leading cause of death around the world. In cancer treatment, over 50% of cancer patients receive radiotherapy alone or in multimodal combinations with other therapies. One of the adverse consequences after radiation exposure is the occurrence of radiation-induced tissue fibrosis (RIF), which is characterized by the abnormal activation of myofibroblasts and the excessive accumulation of extracellular matrix. This phenotype can manifest in multiple organs, such as lung, skin, liver and kidney. In-depth studies on the mechanisms of radiation-induced fibrosis have shown that a variety of extracellular signals such as immune cells and abnormal release of cytokines, and intracellular signals such as cGAS/STING, oxidative stress response, metabolic reprogramming and proteasome pathway activation are involved in the activation of myofibroblasts. Tissue fibrosis is extremely harmful to patients' health and requires early diagnosis. In addition to traditional serum markers, histologic and imaging tests, the diagnostic potential of nuclear medicine techniques is emerging. Anti-inflammatory and antioxidant therapies are the traditional treatments for radiation-induced fibrosis. Recently, some promising therapeutic strategies have emerged, such as stem cell therapy and targeted therapies. However, incomplete knowledge of the mechanisms hinders the treatment of this disease. Here, we also highlight the potential mechanistic, diagnostic and therapeutic directions of radiation-induced fibrosis.

Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume

Background

We use longitudinal chest CT images to explore the effect of steroids therapy in COVID-19 pneumonia which caused pulmonary lesion progression.

Materials and Methods

We retrospectively enrolled 78 patients with severe to critical COVID-19 pneumonia, among which 25 patients (32.1%) who received steroid therapy. Patients were further divided into two groups with severe and significant-severe illness based on clinical symptoms. Serial longitudinal chest CT scans were performed for each patient. Lung tissue was segmented into the five lung lobes and mapped into the five pulmonary tissue type categories based on Hounsfield unit value. The volume changes of normal tissue and pneumonia fibrotic tissue in the entire lung and each five lung lobes were the primary outcomes. In addition, this study calculated the changing percentage of tissue volume relative to baseline value to directly demonstrate the disease progress.

Results

Steroid therapy was associated with the decrease of pneumonia fibrotic tissue (PFT) volume proportion. For example, after four CT cycles of treatment, the volume reduction percentage of PFT in the entire lung was −59.79[±12.4]% for the steroid-treated patients with severe illness, and its p-value was 0.000 compared to that (−27.54[±85.81]%) in non-steroid-treated ones. However, for the patient with a significant-severe illness, PFT reduction in steroid-treated patients was −41.92[±52.26]%, showing a 0.275 p-value compared to −37.18[±76.49]% in non-steroid-treated ones. The PFT evolution analysis in different lung lobes indicated consistent findings as well.

Conclusion

Steroid therapy showed a positive effect on the COVID-19 recovery, and its effect was related to the disease severity.

Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review

Medical imaging is the process of visual representation of different tissues and organs of the human body to monitor the normal and abnormal anatomy and physiology of the body. There are many medical imaging techniques used for this purpose such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and diagnostic sonography. These advanced medical imaging techniques have many applications in the diagnosis of myocardial diseases, cancer of different tissues, neurological disorders, congenital heart disease, abdominal illnesses, complex bone fractures, and other serious medical conditions. There are benefits as well as some risks to every imaging technique. There are some steps for minimizing the radiation exposure risks from imaging techniques. Advance medical imaging modalities such as PET/CT hybrid, three-dimensional ultrasound computed tomography (3D USCT), and simultaneous PET/MRI give high resolution, better reliability, and safety to diagnose, treat, and manage complex patient abnormalities. These techniques ensure the production of new accurate imaging tools with improving resolution, sensitivity, and specificity. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field will become a field of regular measurement of various complex diseases and will provide healthcare solutions.

Filtration-histogram based texture analysis and CALIPER based pattern analysis as quantitative CT techniques in idiopathic pulmonary fibrosis: head-to-head comparison

OBJECTIVE

To assess the prognostic performance of two quantitative CT (qCT) techniques in idiopathic pulmonary fibrosis (IPF) compared to established clinical measures of disease severity (GAP index).

METHODS

Retrospective analysis of high-resolution CT scans for 59 patients (age 70.5 ± 8.8 years) with two qCT methods. Computer-aided lung informatics for pathology evaluation and ratings based analysis classified the lung parenchyma into six different patterns: normal, ground glass, reticulation, hyperlucent, honeycombing and pulmonary vessels. Filtration histogram-based texture analysis extracted texture features: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPPs), skewness and kurtosis at different spatial scale filters. Univariate Kaplan-Meier survival analysis assessed the different qCT parameters' performance to predict patient outcome and refine the standard GAP staging system. Multivariate cox regression analysis assessed the independence of the significant univariate predictors of patient outcome.

RESULTS

The predominant parenchymal lung pattern was reticulation (16.6% ± 13.9), with pulmonary vessel percentage being the most predictive of worse patient outcome (p = 0.009). Higher SD, entropy and MPP, in addition to lower skewness and kurtosis at fine texture scale (SSF2), were the most significant predictors of worse outcome (p < 0.001). Multivariate cox regression analysis demonstrated that SD (SSF2) was the only independent predictor of survival (p < 0.001). Better patient outcome prediction was achieved after adding total vessel percentage and SD (SSF2) to the GAP staging system (p = 0.006).

CONCLUSION

Filtration-histogram texture analysis can be an independent predictor of patient mortality in IPF patients.

ADVANCES IN KNOWLEDGE

qCT analysis can help in risk stratifying IPF patients in addition to clinical markers.

Pulmonary fibrosis model using micro-CT analyzable human PSC-derived alveolar organoids containing alveolar macrophage-like cells

Human lung organoids (hLOs) are useful for disease modelling and drug screening. However, a lack of immune cells in hLOs limits the recapitulation of in vivo cellular physiology. Here, we generated hLOs containing alveolar macrophage (AMφ)–like cells derived from pluripotent stem cells (PSC). To bridge hLOs with advanced human lung high-resolution X-ray computed tomography (CT), we acquired quantitative micro-CT images. Three hLO types were observed during differentiation. Among them, alveolar hLOs highly expressed not only lung epithelial cell markers but also AMφ-specific markers. Furthermore, CD68⁺ AMφ-like cells were spatially organized on the luminal epithelial surface of alveolar hLOs. Bleomycin-treated alveolar hLOs showed upregulated expression of fibrosis-related markers and extracellular matrix deposits in the alveolar sacs. Alveolar hLOs also showed structural alterations such as excessive tissue fraction under bleomycin treatment. Therefore, we suggest that micro-CT analyzable PSC-derived alveolar hLOs are a promising in vitro model to predict lung toxicity manifestations, including fibrosis.

Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

Background

Chest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.

Results

The Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).

Conclusions

The automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

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.

Automated Diseased Lung Volume Percentage Calculation in Quantitative CT Evaluation of Chronic Obstructive Pulmonary Disease and Idiopathic Pulmonary Fibrosis

OBJECTIVE

Several software-based quantitative computed tomography (CT) analysis methods have been developed for assessing emphysema and interstitial lung disease. Although the texture classification method appeared to be more successful than the other methods, the software programs are not commercially available, to our knowledge. Therefore, this study aimed to investigate the usefulness of a commercially available software program for quantitative CT analyses.

METHODS

This prospective cohort study included 80 patients with chronic obstructive pulmonary disease (COPD) or idiopathic pulmonary fibrosis (IPF).

RESULTS

The percentage of low attenuation volume and high attenuation volume had high sensitivity and high specificity for detecting emphysema and pulmonary fibrosis, respectively. The percentage of diseased lung volume (DLV%) was significantly correlated with the lung diffusion capacity for carbon monoxide in all patients with COPD and IPF patients.

CONCLUSIONS

The quantitative CT analysis may improve the precision of the assessment of DLV%, which itself could be a useful tool in predicting lung diffusion capacity in patients with the clinical diagnosis of COPD or IPF.

Protective Effects of Extracellular Matrix-Derived Hydrogels in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with significant gas exchange impairment owing to exaggerated extracellular matrix (ECM) deposition and myofibroblast activation. IPF has no cure, and although nintedanib and pirfenidone are two approved medications for symptom management, the total treatment cost is exuberant and prohibitive to a global uninsured patient population. New therapeutic alternatives with moderate costs are needed to treat IPF. ECM hydrogels derived from decellularized lungs are cost-effective therapeutic candidates to treat pulmonary fibrosis because of their reported antioxidant properties. Oxidative stress contributes to IPF pathophysiology by damaging macromolecules, interfering with tissue remodeling, and contributing to myofibroblast activation. Thus, preventing oxidative stress has beneficial outcomes in IPF. For this purpose, this review describes ECM hydrogel's properties to regulate oxidative stress and tissue remodeling in IPF.

Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches

Our paper proposes a method to measure lung parenchyma parameters from pulmonary window computed tomography images based on ResU-Net model including the CT value, the density, the lung volume, and the surface area of the lungs of healthy rats, to help promote the quantitative analysis of lung parenchyma parameters of rats in medical respiratory researches. Through the analysis of the lung parenchyma parameters of the control group and the treatment group, the law of change among the lung parenchyma parameters is given in our paper. After comparing and analyzing the lung parenchyma parameter CT value and the density of the two groups, it is discovered that the lung parenchyma parameter CT value and the density significantly increase in the treatment group which is after continuously inhaling the nebulization of contrast agents. The change of the lung volume with the surface area in both two groups conforms to the law of lung changes during breathing. The relationship between the lung volume and the CT value or the density is analyzed and it is concluded that the lung volume is negatively correlated with the CT value or the density.

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools

OBJECTIVE

Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients.

METHODS

418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses.

RESULTS

In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92).

CONCLUSIONS

In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.

Objective quantitative multidetector computed tomography assessments in patients with combined pulmonary fibrosis with emphysema: Relationship with pulmonary function and clinical events

BACKGROUND

Combined pulmonary fibrosis with emphysema (CPFE) is a clinically meaningful syndrome characterized by coexisting upper-lobe emphysema and lower-lobe interstitial fibrosis. However, ambiguous diagnostic criteria and, particularly, the absence of objective methods to quantify emphysematous/fibrotic lesions in patients with CPFE confound the interpretation of the pathophysiology of this syndrome. We analyzed the relationship between objectively quantified computed tomography (CT) measurements and the results of pulmonary function testing (PFT) and clinical events in CPFE patients.

MATERIALS AND METHODS

We enrolled 46 CPFE patients who underwent CT and PFT. The extent of emphysematous lesions was obtained by calculating the percent of low attenuation area (%LAA). The extent of fibrotic lesions was calculated as the percent of high attenuation area (%HAA). %LAA and %HAA values were combined to yield the percent of abnormal area (%AA). We assessed the relationships between CT parameters and other clinical indices, including PFT results. Multivariate analysis was performed to examine the association between the CT parameters and clinical events.

RESULTS

A greater negative correlation with percent predicted diffusing capacity of the lung for carbon monoxide (DLCO %predicted) existed for %AA (r = -0.73, p < 0.001) than for %LAA or %HAA alone. The %HAA value was inversely correlated with percent predicted forced vital capacity (r = -0.48, p < 0.001), percent predicted total lung capacity (r = -0.48, p < 0.01), and DLCO %predicted (r = -0.47, p < 0.01). Multivariate logistic regression analysis found that %AA showed the strongest association with hospitalization events (odds ratio = 1.20, 95% confidence interval = 1.01-1.54, p = 0.029).

CONCLUSION

Quantitative CT measurements reflected deterioration in pulmonary function and were associated with hospitalization in patients with CPFE. This approach could serve as a useful method to determine the extent of lung morphology, pathophysiology, and the clinical course of patients with CPFE.

Idiopathic pulmonary fibrosis: Molecular mechanisms and potential treatment approaches

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive disease with high mortality that commonly occurs in middle-aged and older adults. IPF, characterized by a decline in lung function, often manifests as exertional dyspnea and cough. Symptoms result from a fibrotic process driven by alveolar epithelial cells that leads to increased migration, proliferation, and differentiation of lung fibroblasts. Ultimately, the differentiation of fibroblasts into myofibroblasts, which synthesize excessive amounts of extracellular matrix proteins, destroys the lung architecture. However, the factors that induce the fibrotic process are unclear. Diagnosis can be a difficult process; the gold standard for diagnosis is the multidisciplinary conference. Practical biomarkers are needed to improve diagnostic and prognostic accuracy. High-resolution computed tomography typically shows interstitial pneumonia with basal and peripheral honeycombing. Gas exchange and diffusion capacity are impaired. Treatments are limited, although the anti-fibrotic drugs pirfenidone and nintedanib can slow the progression of the disease. Lung transplantation is often contraindicated because of age and comorbidities, but it improves survival when successful. The incidence and prevalence of IPF has been increasing and there is an urgent need for improved therapies. This review covers the detailed cellular and molecular mechanisms underlying IPF progression as well as current treatments and cutting-edge research into new therapeutic targets.

Quantitative CT analysis for bronchiolitis obliterans in perinatally HIV-infected adolescents-comparison with controls and lung function data

OBJECTIVE

To compare quantitative chest CT parameters in perinatally HIV-infected adolescents with and without bronchiolitis obliterans compared with HIV-uninfected controls and their association with lung function measurements.

MATERIALS AND METHODS

Seventy-eight (41 girls) HIV-infected adolescents with a mean age of 13.8 ± 1.65 years and abnormal pulmonary function tests in the prospective Cape Town Adolescent Antiretroviral Cohort underwent contrast-enhanced chest CT on inspiration and expiration. Sixteen age-, sex-, and height-matched non-infected controls were identified retrospectively. Fifty-one HIV-infected adolescents (28 girls) displayed mosaic attenuation on expiration suggesting bronchiolitis obliterans. Pulmonary function tests were collected. The following parameters were obtained: low- and high-attenuation areas, mean lung density, kurtosis, skewness, ventilation heterogeneity, lung mass, and volume.

RESULTS

HIV-infected adolescents showed a significantly higher mean lung density, ventilation heterogeneity, mass, and high- and low-attenuation areas compared with non-infected individuals. Kurtosis and skewness were significantly lower as well. HIV-infected adolescents with bronchiolitis obliterans had a significantly lower kurtosis and skewness compared with those without bronchiolitis obliterans. Lung mass and volume showed the strongest correlations with forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), and alveolar volume. Low-attenuation areas below - 950 HU and ventilation heterogeneity showed the strongest correlation with FEV₁/FVC (range, - 0.51 to - 0.34) and forced expiratory flow between 25 and 75% of FVC (range, - 0.50 to - 0.35).

CONCLUSION

Quantitative chest CT on inspiration is a feasible technique to differentiate perinatally HIV-infected adolescents with and without bronchiolitis obliterans. Quantitative CT parameters correlate with spirometric measurements of small-airway disease.

KEY POINTS

• Perinatally HIV-infected adolescents showed a more heterogeneous attenuation of the lung parenchyma with a higher percentage of low- and high-attenuation areas compared with non-infected patients. • Kurtosis and skewness are able to differentiate between HIV-infected adolescents with and without bronchiolitis obliterans using an inspiratory chest CT. • Quantitative CT parameters of the chest correlate significantly with pulmonary function test. Low-attenuation areas and ventilation heterogeneity are particularly associated with spirometric parameters related to airway obstruction.

Early decrease in erector spinae muscle area and future risk of mortality in idiopathic pulmonary fibrosis

Computed tomography (CT) assessment of the cross-sectional area of the erector spinae muscles (ESM_CSA) can be used to evaluate sarcopenia and cachexia in patients with lung diseases. This study aimed to confirm whether serial changes in ESM_CSA are associated with survival in patients with idiopathic pulmonary fibrosis (IPF). Data from consecutive patients with IPF who were referred to a single centre were retrospectively reviewed. We measured the ESM_CSA at the level of the 12th thoracic vertebra on CT images at referral and 6 months later (n = 119). The follow-up time was from 817–1633 days (median, 1335 days) and 59 patients (49.6%) died. A univariate Cox regression analysis showed that the decline in % predicted forced vital capacity (FVC) (Hazard ratios [HR] 1.041, 95% confidence interval [CI] 1.013–1.069, P = 0.004), the decline in body mass index (BMI) (HR 1.084, 95% CI 1.037–1.128; P < 0.001) and that in ESM_CSA (HR 1.057, 95% CI 1.027–1.086; P < 0.001) were prognostic factors. For multivariate analyses, the decline in ESM_CSA (HR 1.039, 95% CI 1.007–1.071, P = 0.015) was a significant prognostic factor, while those in % FVC and BMI were discarded. Early decrease in ESM_CSA may be a useful predictor of prognosis in patients with IPF.

[Artificial intelligence in lung imaging]

CLINICAL/METHODICAL ISSUE

Artificial intelligence (AI) has the potential to improve diagnostic accuracy and management in patients with lung disease through automated detection, quantification, classification, and prediction of disease progression.

STANDARD RADIOLOGICAL METHODS

Owing to unspecific symptoms, few well-defined CT disease patterns, and varying prognosis, interstitial lungs disease represents a focus of AI-based research.

METHODICAL INNOVATIONS

Supervised and unsupervised machine learning can identify CT disease patterns using features which may allow the analysis of associations with specific diseases and outcomes.

PERFORMANCE

Machine learning on the one hand improves computer-aided detection of pulmonary nodules. On the other hand it enables further characterization of pulmonary nodules, which may improve resource effectiveness regarding lung cancer screening programs.

ACHIEVEMENTS

There are several challenges regarding AI-based CT data analysis. Besides the need for powerful algorithms, expert annotations and extensive training data sets that reflect physiologic and pathologic variability are required for effective machine learning. Comparability and reproducibility of AI research deserve consideration due to a lack of standardization in this emerging field.

PRACTICAL RECOMMENDATIONS

This review article presents the state of the art and the challenges concerning AI in lung imaging with special consideration of interstitial lung disease, and detection and consideration of pulmonary nodules.

Visual vs. computer-based computed tomography analysis for the identification of functional patterns in interstitial lung diseases

PURPOSE OF REVIEW

Computer algorithms possess an intrinsic speed, objectivity, reproducibility and scalability unmatched by visual quantitation methods performed by trained readers. The question of how well quantitative CT (QCT) analysis methods compare with visual CT analysis to predict functional status in fibrosing lung diseases (FLDs) is of increasing relevance to understand the future role QCT may have in prognostication of FLD.

RECENT FINDINGS

The latest computer algorithms demonstrate improved performance over visual CT analysis in predicting baseline disease severity as measured by correlations with functional indices of lung damage. QCT analysis may, therefore, have a role in aiding clinical decision-making as well as in the enrichment of drug trial populations. Quantitative analysis on longitudinal CTs has also shown better correlations with changes in functional indices whenever compared with visual scores of change suggesting the potential of QCT analysis as an imaging biomarker of disease progression in FLD. Importantly, computer algorithms are now able to identify prognostic imaging biomarkers that cannot be quantified visually (e.g. vessel-related structures).

SUMMARY

QCT holds great promise for the evaluation of damage in FLD. Challenges for QCT include accommodating measurement noise from variation in CT acquisition techniques and developing patient-friendly visualizations of quantitative outputs.

Translation of Quantitative Imaging Biomarkers into Clinical Chest CT

Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ^©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).

Predicting clinical outcome with phenotypic clusters using quantitative CT fibrosis and emphysema features in patients with idiopathic pulmonary fibrosis

Background

The clinical course of IPF varies. This study sought to identify phenotyping with quantitative computed tomography (CT) fibrosis and emphysema features using a cluster analysis and to assess prognostic impact among identified clusters in patient with idiopathic pulmonary fibrosis (IPF). Furthermore, we evaluated the impact of fibrosis and emphysema on lung function with development of a descriptive formula.

Methods

This retrospective study included 205 patients with IPF. A texture-based automated system was used to quantify areas of normal, emphysema, ground-glass opacity, reticulation, consolidation, and honeycombing. Emphysema index was obtained by calculating the percentage of low attenuation area lower than -950HU. We used quantitative CT features and clinical features for clusters and assessed the association with prognosis. A formula was derived using fibrotic score and emphysema index on quantitative CT.

Results

Three clusters were identified in IPF patients using a quantitative CT score and clinical values. Prognosis was better in cluster1, with a low extent of fibrosis and emphysema with high forced vital capacity (FVC) than cluster2 and cluster3 with higher fibrotic score and emphysema (p = 0.046, and p = 0.026). In the developed formula [1.5670—fibrotic score(%)*0.04737—emphysema index*0.00304], a score greater ≥ 0 indicates coexisting of pulmonary fibrosis and emphysema at a significant extent despite of normal spirometric result.

Conclusions

Cluster analysis identified distinct phenotypes, which predicted prognosis of clinical outcome. Formula using quantitative CT values is useful to assess extent of pulmonary fibrosis and emphysema with normal lung function in patients with IPF.

Quantitative CT analysis of honeycombing area predicts mortality in idiopathic pulmonary fibrosis with definite usual interstitial pneumonia pattern: A retrospective cohort study

Background

Honeycombing on high-resolution computed tomography (HRCT) images is a key finding in idiopathic pulmonary fibrosis (IPF). In IPF, honeycombing area determined by quantitative CT analysis is correlated with pulmonary function test findings. We hypothesized that quantitative CT-derived honeycombing area (HA) might predict mortality in patients with IPF.

Materials and methods

Chest HRCT images of 52 IPF patients with definite usual interstitial pneumonia (UIP) pattern were retrospectively evaluated. Mortality data up to July 31, 2016, were recorded. Using a computer-aided system, HA and percentage of HA (%HA) were measured quantitatively. Predictors of 3-year mortality were evaluated using logistic regression models.

Results

The median %HA, %predicted forced vital capacity (FVC) and composite physiologic index (CPI) were 3.8%, 83.6%, and 33.6, respectively. According to GAP (gender, age, and physiology) stage, 20, 14, and 5 patients were classified under stages I-II-III, respectively. Percentage of HA was significantly correlated with %FVC, CPI, and GAP stage (all, p < 0.001). In univariate analysis, %HA, %FVC, and CPI were statistically significant predictors of mortality. In multivariate analysis using the stepwise regression method, only %HA (odds ratio [OR], 1.27; p = 0.011) was a significant independent predictors of mortality. Patients with %HA ≥ 4.8% had significantly lower survival rates than those with lesser %HA (median survival time, 1.3 vs 5.0 years; log-rank test; p < 0.001).

Conclusion

Quantitative CT-derived HA might be an important and independent predictor of mortality in IPF patients with definite UIP pattern.

Idiopathic Pulmonary Fibrosis (IPF): An Overview

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterised by chronic, progressive scarring of the lungs and the pathological hallmark of usual interstitial pneumonia. Current paradigms suggest alveolar epithelial cell damage is a key initiating factor. Globally, incidence of the disease is rising, with associated high morbidity, mortality, and economic healthcare burden. Diagnosis relies on a multidisciplinary team approach with exclusion of other causes of interstitial lung disease. Over recent years, two novel antifibrotic therapies, pirfenidone and nintedanib, have been developed, providing treatment options for many patients with IPF, with several other agents in early clinical trials. Current efforts are directed at identifying key biomarkers that may direct more customized patient-centred healthcare to improve outcomes for these patients in the future.

Organ and tissue fibrosis: Molecular signals, cellular mechanisms and translational implications

Fibrosis denotes excessive scarring, which exceeds the normal wound healing response to injury in many tissues. Although the extracellular matrix deposition appears unstructured disrupting the normal tissue architecture and subsequently impairing proper organ function, fibrogenesis is a highly orchestrated process determined by defined sequences of molecular signals and cellular response mechanisms. Persistent injury and parenchymal cell death provokes tissue inflammation, macrophage activation and immune cell infiltration. The release of biologically highly active soluble mediators (alarmins, cytokines, chemokines) lead to the local activation of collagen producing mesenchymal cells such as pericytes, myofibroblasts or Gli1 positive mesenchymal stem cell-like cells, to a transition of various cell types into myofibroblasts as well as to the recruitment of fibroblast precursors. Clinical observations and experimental models highlighted that fibrosis is not a one-way road. Specific mechanistic principles of fibrosis regression involve the resolution of chronic tissue injury, the shift of inflammatory processes towards recovery, deactivation of myofibroblasts and finally fibrolysis of excess matrix scaffold. The thorough understanding of common principles of fibrogenic molecular signals and cellular mechanisms in various organs - such as liver, kidney, lung, heart or skin - is the basis for developing improved diagnostics including biomarkers or imaging techniques and novel antifibrotic therapeutics.