Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion

OBJECTIVE

To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (QCT) measures.

METHODS

Patients with idiopathic pulmonary fibrosis (IPF) who underwent unenhanced chest CTs with vendor A and a pulmonary function test (PFT) were retrospectively evaluated. As deep-learning based ILD quantification software was mainly developed using vendor B CT, style-converted images from vendor A to B style were generated using RouteGAN. Quantification was performed in both original and converted images. Measurement variability in QCT between original and converted images was evaluated using the concordance correlation coefficient (CCC). Two radiologists visually evaluated quantification accuracy using original and converted images. Correlations between CT parameters and PFT measures were assessed.

RESULTS

Total 112 patients (mean age, 61; 82 men) were studied. Measurement variability between original and converted CT was a CCC of 0.20 for reticulation, 0.72 for honeycombing, and 0.59 for ground-glass opacity. The median visual accuracy scores were higher for the quantification using converted compared with the original images (P < 0.001). Correlation between fibrosis score increased significantly after CT conversion for both forced vital capacity (original vs. converted; -0.35 vs. -0.50; P = 0.005) and diffusing capacity of the lung for carbon monoxide (-0.50 vs. -0.66; P < 0.001).

CONCLUSION

The improved accuracy in deep learning based ILD quantification after applying GAN-based CT style conversion can result in the improved functional correlation of QCT measurements in patients with IPF.

Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis

Automated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing. Opacity-based fibrotic lung (OFL) was measured by a second tool that performs a simpler binary classification of tissue into either normal or opacified lung and was originally developed for quantifying pneumonia. Differences in quantification of MTFL and OFL were assessed by Mann-Whitney U-test and Pearson correlation (r). Also, correlation with spirometry parameters (percent predicted total lung capacity (TLC), percent predicted vital capacity (VC), percent predicted forced expiratory volume in 1 s (FEV1), diffusing capacity of the lungs for carbon monoxide (DLCO), partial pressure of oxygen (PO2) and carbon dioxide (PCO2)) were assessed by r. The prognostic values for 3-year patient survival of OFL, LSS and MTFL were investigated by multivariable Cox-proportional-hazards (CPH) models including sex, age and TLC and including sex, age and VC. Also, Kaplan-Meier analysis with log rank test between subgroups separated by median OFL and MTFL were conducted. No significant difference between OFL and MTFL was observed (median and interquartile range: OFL = 29% [20-38%], MTFL = 31% [19-45%]; P = 0.44). For OFL significant correlation was observed to MTFL (r = 0.93, P < 0.01) and VC (r=-0.50, P = 0.03). For MTFL no significant correlation to spirometry parameters was found. The total time for one analysis was lower for the automated MTFL (MTFL: 313 ± 25s vs. OFL: 612 ± 61s, P < 0.001). Both analyses were significant predictors in the multivariable CPH analysis including TLC (hazard-ratios: MTFL 1.03 [1.01-1.06], P = 0.02; OFL 1.03 [1.00-1.06], P = 0.03). No parameter was a significant predictor in the CPH models including VC (hazard-ratios: MTFL 1.01 [0.98-1.04], P = 1; OFL 1.01 [0.97-1.05], P = 1). OFL showed significance in Kaplan-Meier analysis (MTFL: P = 0.17; OFL: P = 0.03). Using a simple opacity-based quantification of pulmonary fibrosis in IPF patients displayed similar results and prognostic value compared to a more complex multi-texture based analysis.

CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions

ABSTRACT

Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Usefulness of CT Quantification-Based Assessment in Defining Progressive Pulmonary Fibrosis

RATIONALE AND OBJECTIVES

To establish a quantitative CT threshold for radiological disease progression of progressive pulmonary fibrosis (PPF) and evaluate its feasibility in patients with connective tissue disease-related interstitial lung disease (CTD-ILD).

MATERIALS AND METHODS

Between April 2007 and October 2022, patients diagnosed with CTD-ILD retrospectively evaluated. CT quantification was conducted using a commercial software by summing the percentages of ground-glass opacity, consolidation, reticular opacity, and honeycombing. The quantitative threshold for radiological progression was determined based on the highest discrimination on overall survival (OS). Two thoracic radiologists independently evaluated visual radiological progression, and the senior radiologist's assessment was used as the final result. Cox regression was used to assess prognosis of PPF based on the visual assessment and quantitative threshold.

RESULTS

97 patients were included and followed up for a median of 30.3 months (range, 4.7-198.1 months). For defining radiological disease progression, the optimal quantitative CT threshold was 4%. Using this threshold, 12 patients were diagnosed with PPF, while 14 patients were diagnosed with PPF based on the visual assessment, with an agreement rate of 97.9% (95/97). Worsening respiratory symptoms (hazard ratio [HR], 12.73; P < .001), PPF based on the visual assessment (HR, 8.86; P = .002) and based on the quantitative threshold (HR, 6.72; P = .009) were independent risk factors for poor OS.

CONCLUSION

The quantitative CT threshold for radiological disease progression (4%) was feasible in defining PPF in terms of its agreement with PPF grouping and prognostic performance when compared to visual assessment.

Lung cell transplantation for pulmonary fibrosis

Idiopathic pulmonary fibrosis is a major cause of death with few treatment options. Here, we demonstrate the therapeutic efficacy for lung fibrosis of adult lung cell transplantation using a single-cell suspension of the entire lung in two distinct mouse systems: bleomycin treatment and mice lacking telomeric repeat-binding factor 1 expression in alveolar type 2 (AT2) cells (SPC-Cre TRF1fl/fl), spontaneously developing fibrosis. In both models, the progression of fibrosis was associated with reduced levels of host lung progenitors, enabling engraftment of donor progenitors without any additional conditioning, in contrast to our previous studies. Two months after transplantation, engrafted progenitors expanded to form numerous donor-derived patches comprising AT1 and AT2 alveolar cells, as well as donor-derived mesenchymal and endothelial cells. This lung chimerism was associated with attenuation of fibrosis, as demonstrated histologically, biochemically, by computed tomography imaging, and by lung function measurements. Our study provides a strong rationale for the treatment of lung fibrosis using lung cell transplantation.

Clustering analysis of HRCT parameters measured using a texture-based automated system: relationship with clinical outcomes of IPF

PURPOSE

The extent of honeycombing and reticulation predict the clinical prognosis of IPF. Emphysema, consolidation, and ground glass opacity are visible in HRCT scans. To date, there have been few comprehensive studies that have used these parameters. We conducted automated quantitative analysis to identify predictive parameters for clinical outcomes and then grouped the subjects accordingly.

METHODS

CT images were obtained while patients held their breath at full inspiration. Parameters were analyzed using an automated lung texture quantification system. Cluster analysis was conducted on 159 IPF patients and clinical profiles were compared between clusters in terms of survival.

RESULTS

Kaplan-Meier analysis revealed that survival rates declined as fibrosis, reticulation, honeycombing, consolidation, and emphysema scores increased. Cox regression analysis revealed that reticulation had the most significant impact on survival rate, followed by honeycombing, consolidation, and emphysema scores. Hierarchical and K-means cluster analyses revealed 3 clusters. Cluster 1 (n = 126) with the lowest values for all parameters had the longest survival duration, and relatively-well preserved FVC and DLCO. Cluster 2 (n = 15) with high reticulation and consolidation scores had the lowest FVC and DLCO values with a predominance of female, while cluster 3 (n = 18) with high honeycombing and emphysema scores predominantly consisted of male smokers. Kaplan-Meier analysis revealed that cluster 2 had the lowest survival rate, followed by cluster 3 and cluster 1.

CONCLUSION

Automated quantitative CT analysis provides valuable information for predicting clinical outcomes, and clustering based on these parameters may help identify the high-risk group for management.

Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach

OBJECTIVES

To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications.

METHODS

This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning-based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis). Patients were grouped according to the absolute decline of predicted FVC (< 5%, 5-10%, and ≥ 10%) and ILD progression assessed by thoracic radiologists, and their quantification results were compared among these groups. The associations between quantification results and survival were evaluated using multivariable Cox regression analysis.

RESULTS

In total, 468 patients (239 men; 64 ± 9.5 years) were included. Fibrosis and total ILD extents more increased in patients with larger FVC decline (p < .001 in both). Patients with ILD progression had higher fibrosis and total ILD extent increases than those without ILD progression (p < .001 in both). Increases in fibrosis and total ILD extent were significant prognostic factors when adjusted for absolute FVC declines of ≥ 5% (hazard ratio [HR] 1.844, p = .01 for fibrosis; HR 2.484, p < .001 for total ILD extent) and ≥ 10% (HR 2.918, p < .001 for fibrosis; HR 3.125, p < .001 for total ILD extent).

CONCLUSION

Changes in ILD CT quantification correlated with changes in FVC and visual assessment of ILD progression, and they were independent prognostic factors in ILD patients.

CLINICAL RELEVANCE STATEMENT

Quantifying the CT features of interstitial lung disease using deep learning techniques could play a key role in defining and predicting the prognosis of progressive fibrosing interstitial lung disease.

KEY POINTS

• Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease. • Deep learning-based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors. • Deep learning-based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease.

Prognostic implication of 1-year decline in diffusing capacity in newly diagnosed idiopathic pulmonary fibrosis

The progression of idiopathic pulmonary fibrosis (IPF) is assessed through serial monitoring of forced vital capacity (FVC). Currently, data regarding the clinical significance of longitudinal changes in diffusing capacity for carbon monoxide (DLCO) is lacking. We investigated the prognostic implications of a 1-year decline in DLCO in 319 patients newly diagnosed with IPF at a tertiary hospital between January 2010 and December 2020. Changes in FVC and DLCO over the first year after the initial diagnosis were reviewed; a decline in FVC ≥ 5% and DLCO ≥ 10% predicted were considered significant changes. During the first year after diagnosis, a significant decline in FVC and DLCO was observed in 101 (31.7%) and 64 (20.1%) patients, respectively. Multivariable analysis showed that a 1-year decline in FVC ≥ 5% predicted (aHR 2.74, 95% CI 1.88-4.00) and 1-year decline in DLCO ≥ 10% predicted (aHR 2.31, 95% CI 1.47-3.62) were independently associated with a higher risk of subsequent mortality. The prognostic impact of a decline in DLCO remained significant regardless of changes in FVC, presence of emphysema, or radiographic indications of pulmonary hypertension. Therefore, serial monitoring of DLCO should be recommended because it may offer additional prognostic information compared with monitoring of FVC alone.

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

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

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease

The shortcomings of qualitative visual assessment have led to the development of computer-based tools to characterise and quantify disease on high-resolution computed tomography (HRCT) in patients with interstitial lung diseases (ILDs). Quantitative CT (QCT) software enables quantification of patterns on HRCT with results that are objective, reproducible, sensitive to change and predictive of disease progression. Applications developed to provide a diagnosis or pattern classification are mainly based on artificial intelligence. Deep learning, which identifies patterns in high-dimensional data and maps them to segmentations or outcomes, can be used to identify the imaging patterns that most accurately predict disease progression. Optimisation of QCT software will require the implementation of protocol standards to generate data of sufficient quality for use in computerised applications and the identification of diagnostic, imaging and physiological features that are robustly associated with mortality for use as anchors in the development of algorithms. Consortia such as the Open Source Imaging Consortium have a key role to play in the collation of imaging and clinical data that can be used to identify digital imaging biomarkers that inform diagnosis, prognosis and response to therapy.

Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images

BACKGROUND

Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability.

OBJECTIVE

To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD.

METHODS

In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions.

RESULTS

Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation.

CONCLUSIONS

In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.

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

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

Measure of lung dielectric proprieties in patients with Idiopathic Pulmonary Fibrosis: correlation with clinical, radiological and pulmonary functional parameters

BACKGROUND

Dielectric properties of biological tissues are biophysical parameters; in lung they change with amount of air, blood and parenchyma. Remote Dielectric Sensing (ReDS™) technology measures dielectric properties of lung tissues quantifying the content of fluids inside the scan volume. We aimed to evaluate the reliability of ReDS™ measure in Idiopathic Pulmonary Fibrosis (IPF) patients and in healthy volunteers, and to investigate the correlation of ReDS™ score with clinical, radiological and functional parameters.

METHODS

We conducted a prospective observational study, including 52 patients with diagnosis of IPF and 17 healthy volunteers; for each patient we recorded: complete functional evaluation, dyspnoea score (mMRC scale), Usual Interstitial Pneumonia (UIP) Computed Tomography (CT) pattern (UIP definite or probable) and ReDS™ measure (expressed in %).

RESULTS

ReDS™ measure was reported as correct both in patients and controls, the firsts with higher scores (33.8% vs 29.1%, p = 0.003). In IPF patients we observed a significant inverse correlation with ReDS™ score and Forced Vital Capacity (FVC), Vital Capacity (VC) and Total Lung Capacity (TLC) measures and, when we considered only patients with UIP definite CT pattern, the correlation was inverse with FVC, VC, TLC, DLCO. In IPF patients the higher was mMRC dyspnoea index, the higher was ReDS™ score. No significant correlations were observed between ReDS™ score and functional parameters in healthy controls.

DISCUSSION

We demonstrated a correlation of ReDS™ scores with some functional (mainly indicative or diagnostic for restriction) and clinical parameters in IPF patients; the score was correlated with density of tissues possibly quantifying tissue fibrosis in IPF patients.

Radiological findings based comparison of functional status in patients who have post-covid lung injury or idiopathic pulmonary fibrosis

BACKGROUND

Following COVID-19 infection, some patients acquired lung injury and fibrosis. Idiopathic pulmonary fibrosis is characterized by lung fibrosis. Both post-COVID lung injury and idiopathic pulmonary fibrosis cause loss of respiratory function and involvement of the lung parenchyma. We aimed to compare respiratory related functional characteristics and radiological involvement between post-COVID lung injury and idiopathic pulmonary fibrosis.

METHODS

A single center, cross-sectional study was applied. Patients with post-COVID lung injury and idiopathic pulmonary fibrosis included in the study. All patients underwent the 6-minute walk test, as well as the Borg and MRC scales. Radiological images were evaluated and scored for lung parenchymal involvement. The impact of post-COVID lung injury and idiopathic pulmonary fibrosis on respiratory functions of were compared. The relationship of functional status and radiological involvement, as well as the effect of potential confounding factors were investigated.

RESULTS

A total of 71 patients were included in the study. Forty-eight (67.6%) of the patients were male and the mean age was 65.4 ± 10.3 years. Patients with post-COVID lung injury had greater 6-minute walk test distance and duration, as well as higher oxygen saturations. The MRC and Borg dyspnea scores were comparable. At radiologic evaluation, ground glass opacity scores were higher in patients with post-COVID lung injury, whereas pulmonary fibrosis scores were higher in patients with idiopathic pulmonary fibrosis. However, the total severity scores were similar. While pulmonary fibrosis score was found to have a negative correlation with 6-minute walk test distance, test duration, and pre- and post-test oxygen saturation levels, there was a positive correlation with oxygen saturation recovery time and MRC score. There was no relationship between ground glass opacity and the functional parameters.

CONCLUSIONS

Despite having equal degrees of radiological involvement and dyspnea symptom severity, PCLI patients exhibited higher levels of functional status. This might be due to different pathophysiological mechanisms and radiological involvement patterns of both diseases.

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

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

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

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

Prediction model for patient prognosis in idiopathic pulmonary fibrosis using hybrid radiomics analysis

Objectives

To develop an imaging prognostic model for idiopathic pulmonary fibrosis (IPF) patients using hybrid auto-segmentation radiomics analysis, and compare the predictive ability between the radiomics analysis and conventional visual score methods.

Methods

Data from 72 IPF patients who had undergone CT were analyzed. In the radiomics analysis, quantitative CT analysis was performed using the semi-auto-segmentation method. In the visual method, the extent of radiologic abnormalities was evaluated and the overall percentage of lung involvement was calculated by averaging values for six lung zones. Using a training cohort of 50 cases, we generated a radiomics model and a visual score model. Subsequently, we investigated the predictive ability of these models in a testing cohort of 22 cases.

Results

Three significant prognostic factors such as contrast, Idn, and cluster shade were selected by LASSO Cox regression analysis. In the visual method, multivariate Cox regression analysis revealed that honeycombing and reticulation were significant prognostic factors. Subsequently, a predictive nomogram for prognosis in IPF patients was established using these factors. In the testing cohort, the c-index of the visual and radiomics nomograms were 0.68 and 0.74, respectively. When dividing the cohort into high-risk and low-risk groups using the median nomogram score, significant differences in overall survival (OS) in the visual and radiomics models were observed (P=0.000 and P=0.0003, respectively).

Conclusions

The prediction model with hybrid radiomics analysis had a better ability to predict OS in IPF patients than that of the visual method.

Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis

PURPOSE OF REVIEW

The aim of this study was to summarize quantitative computed tomography (CT) and machine learning data in fibrotic lung disease and to explore the potential application of these technologies in pulmonary sarcoidosis.

RECENT FINDINGS

Recent data in the use of quantitative CT in fibrotic interstitial lung disease (ILD) are covered. Machine learning includes deep learning, a branch of machine learning particularly suited to medical imaging analysis. Deep learning imaging biomarker research in ILD is currently undergoing accelerated development, driven by technological advances in image processing and analysis. Fundamental concepts and goals related to deep learning imaging research in ILD are discussed. Recent work highlighted in this review has been performed in patients with idiopathic pulmonary fibrosis (IPF). Quantitative CT and deep learning have not been applied to pulmonary sarcoidosis, although there are recent deep learning data in cardiac sarcoidosis.

SUMMARY

Pulmonary sarcoidosis presents unsolved problems for which quantitative CT and deep learning may provide unique solutions: in particular, the exploration of the long-standing question of whether sarcoidosis should be viewed as a single disease or as an umbrella term for disorders that might usefully be considered as separate diseases.

Texture analysis imaging "what a clinical radiologist needs to know"

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.

Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease

Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess chronic obstructive pulmonary disease (COPD). However, different image pre-processing methods are commonly used, and how these different methods impact radiomics features and lung disease assessment, is unknown. The purpose of this study was to develop an image pre-processing pipeline to investigate how various pre-processing combinations impact radiomics features and their use for COPD assessment. Spirometry and CT images were obtained from the multi-centered Canadian Cohort of Obstructive Lung Disease study. Participants were divided based on assessment site and were further dichotomized as No COPD or COPD within their participant groups. An image pre-processing pipeline was developed, calculating 32 grey level co-occurrence matrix radiomics features. The pipeline included lung segmentation, airway segmentation or no segmentation, image resampling or no resampling, and either no pre-processing, binning, edgmentation, or thresholding pre-processing techniques. A three-way analysis of variance was used for method comparison. A nested 10-fold cross validation using logistic regression and multiple linear regression models were constructed to classify COPD and assess correlation with lung function, respectively. Logistic regression performance was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 1210 participants (Sites 1-8: No COPD:n = 447, COPD:n = 413; and Site 9: No COPD:n = 155, COPD:n = 195) were evaluated. Between the two participant groups, at least 16/32 features were different between airway segmentation/no segmentation (P ≤ 0.04), at least 29/32 features were different between no resampling/resampling (P ≤ 0.04), and 32/32 features were different between the pre-processing techniques (P < 0.0001). Features generated using the resampling/edgmentation and resampling/thresholding pre-processing combinations, regardless of airway segmentation, performed the best in COPD classification (AUC ≥ 0.718), and explained the most variance with lung function (R² ≥ 0.353). Therefore, the image pre-processing methods completed prior to CT radiomics feature extraction significantly impacted extracted features and their ability to assess COPD.

Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT

AIM

To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) to pulmonary function testing (PFT) in assessing idiopathic pulmonary fibrosis (IPF) for patients undergoing treatment and determine the effects of limited (LD) and ultra-low dose (ULD) CT on CALIPER performance.

MATERIALS AND METHODS

Thirty-eight IPF patients underwent PFT and standard, LD, and ULD CT. CALIPER classified each CT voxel into either vessel-related structures (VRS), normal, reticular (R), honeycomb (HC) or ground-glass (GG) features. CALIPER-derived interstitial lung disease (ILD) extent represented the sum of GG, R and HC values. Repeated-measures correlation coefficient (ρ_rm) and 95% confidence interval (CI) evaluated CALIPER features correlation with PFT. Lin's concordance correlation coefficient (CCC) assessed concordance of CALIPER parameters across different CT dosages.

RESULTS

Twenty patients completed 12 months of follow-up. CALIPER ILD correlated significantly with percent predicted (%) forced vital capacity (FVC) and forced expiratory volume in 1 second (%FEV1; p=0.004, ρ_rm -0.343, 95% CI [-0.547, -0.108] and 0.008, -0.321, [-0.518, -0.07], respectively). VRS significantly correlated with %FVC and %FEV1 (p=0.000, ρ_rm -0.491, 95% CI [-0.685, -0.251] and -0.478, 0.000, [-0.653, -0.231], respectively). There was near perfect LD and moderate ULD concordance with standard dose CT for both ILD (CCC 0.995, 95% CI 0.988-0.999 and 0.9, 0.795-0.983, respectively) and VRS (CCC 0.989, 95% CI 0.963-0.997 and 0.915, 0.806-0.956, respectively).

CONCLUSIONS

CALIPER parameters correlate well with PFTs for evaluation of IPF in patients undergoing anti-fibrotic treatment without being influenced by dose variation. CALIPER may serve as a robust, objective adjunct to PFTs in assessing anti-fibrotic treatment related changes.

Interstitial Lung Abnormalities: State of the Art

The clinical importance of interstitial lung abnormality (ILA) is increasingly recognized. In July 2020, the Fleischner Society published a position paper about ILA. The purposes of this article are to summarize the definition, existing evidence, clinical management, and unresolved issues for ILA from a radiologic standpoint and to provide a practical guide for radiologists. ILA is a common incidental finding at CT and is often progressive and associated with worsened clinical outcomes. The hazard ratios for mortality range from 1.3 to 2.7 in large cohorts. Risk factors for ILA include age, smoking status, other inhalational exposures, and genetic factors (eg, gene encoding mucin 5B variant). Radiologists should systematically record the presence, morphologic characteristics, distribution, and subcategories of ILA (ie, nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic), as these are informative for predicting progression and mortality. Clinically significant interstitial lung disease should not be considered ILA. Individuals with ILA are triaged into higher- and lower-risk groups depending on their risk factors for progression, and systematic follow-up, including CT, should be considered for the higher-risk group. Artificial intelligence-based automated analysis for ILA may be helpful, but further validation and improvement are needed. Radiologists have a central role in clinical management and research on ILA.

Interstitial lung disease in systemic sclerosis quantification of disease classification and progression with high-resolution computed tomography: An observational study

INTRODUCTION

Systemic sclerosis-associated interstitial lung disease accounts for up to 20% of mortality in these patients and has a highly variable prognosis. Functional respiratory imaging, a quantitative computed tomography imaging technique which allows mapping of regional information, can provide a detailed view of lung structures. It thereby shows potential to better characterize this disease.

PURPOSE

To evaluate the use of functional respiratory imaging quantitative computed tomography in systemic sclerosis-associated interstitial lung disease staging, as well as the relationship between short-term changes in pulmonary function tests and functional respiratory imaging quantitative computed tomography with respect to disease severity.

MATERIALS AND METHODS

An observational cohort of 35 patients with systemic sclerosis was retrospectively studied by comparing serial pulmonary function tests and in- and expiratory high-resolution computed tomography over 1.5-year interval. After classification into moderate to severe lung disease and limited lung disease (using a hybrid method integrating quantitative computed tomography and pulmonary function tests), post hoc analysis was performed using mixed-effects models and estimated marginal means in terms of functional respiratory imaging parameters.

RESULTS

At follow-up, relative mean forced vital capacity percentage change was not significantly different in the limited (6.37%; N = 13; p = 0.053) and moderate to severe disease (-3.54%; N = 16; p = 0.102) groups, respectively. Specific airway resistance decreased from baseline for both groups. (Least square mean changes -25.11% predicted (p = 0.006) and -14.02% predicted (p = 0.001) for limited and moderate to severe diseases.) In contrast to limited disease from baseline, specific airway radius increased in moderate to severe disease by 8.57% predicted (p = 0.011) with decline of lower lobe volumes of 2.97% predicted (p = 0.031).

CONCLUSION

Functional respiratory imaging is able to differentiate moderate to severe disease versus limited disease and to detect disease progression in systemic sclerosis.

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

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

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

Modern pulmonary imaging of bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is a complex and serious cardiopulmonary morbidity in infants who are born preterm. Despite advances in clinical care, BPD remains a significant source of morbidity and mortality, due in large part to the increased survival of extremely preterm infants. There are few strong early prognostic indicators of BPD or its later outcomes, and evidence for the usage and timing of various interventions is minimal. As a result, clinical management is often imprecise. In this review, we highlight cutting-edge methods and findings from recent pulmonary imaging research that have high translational value. Further, we discuss the potential role that various radiological modalities may play in early risk stratification for development of BPD and in guiding treatment strategies of BPD when employed in varying severities and time-points throughout the neonatal disease course.

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

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

Evaluation of pulmonary ventilation in COVID-19 patients using oxygen-enhanced three-dimensional ultrashort echo time MRI: a preliminary study

AIM

To evaluate the lung function of coronavirus disease 2019 (COVID-19) patients using oxygen-enhanced (OE) ultrashort echo time (UTE) MRI.

MATERIALS AND METHODS

Forty-nine patients with COVID-19 were included in the study. The OE-MRI was based on a respiratory-gated three-dimensional (3D) radial UTE sequence. For each patient, the percent signal enhancement (PSE) map was calculated using the expression PSE = (S_100% – S_21%)/S_21%, where S_21% and S_100% are signals acquired during room air and 100% oxygen inhalation, respectively. Agreement of lesion detectability between UTE-MRI and computed tomography (CT) was performed using the kappa test. The Mann–Whitney U-test was used to evaluate the difference in the mean PSE between mild-type COVID-19 and common-type COVID-19. Spearman's test was used to assess the relationship between lesion mean PSE and lesion size. Furthermore, the Mann–Whitney U-test was used to evaluate the difference in region of interest (ROI) mean PSE between normal pulmonary parenchyma and lesions. The Kruskal–Wallis test was applied to test the difference in the mean PSE between different lesion types.

RESULTS

CT and UTE-MRI reached good agreement in lesion detectability. Ventilation measures in mild-type patients (5.3 ± 5.5%) were significantly different from those in common-type patients (3 ± 3.9%). Besides, there was no significant correlation between lesion mean PSE and lesion size. The mean PSE of COVID-19 lesions (3.2 ± 4.9%) was significantly lower than that of the pulmonary parenchyma (5.4 ± 3.9%). No significant difference was found among different lesion types.

CONCLUSION

OE-UTE-MRI could serve as a promising method for the assessment of lung function or treatment management of COVID-19 patients.

Visual and quantitative assessment of HRCT pulmonary changes in idiopathic interstitial pneumonia with PFT correlation

Background

Our study was designed to correlate the degree of parenchymal affection in idiopathic interstitial pneumonia using visual and semi-quantitative HRCT assessment with pulmonary function test results.

The study involved 50 patients diagnosed as idiopathic interstitial pneumonia. They were referred from a chest outpatient clinic to the Radiology Department in the Faculty of Medicine, Cairo University for HRCT assessment in the period from January 2017 to March 2019. Variable lung parenchymal affection was studied using HRCT and variable post acquisition processing (multi-planar reconstruction, volumetric assessment, 3D color-coded images).

Results

Usual interstitial pneumonia was the most common type of IP, found in approximately 40 patients (80% of cases) followed by nonspecific interstitial pneumonia found in 5 patients (10% of cases) and lymphocytic interstitial pneumonia found in 3 patients (6% of cases), and desquamative interstitial pneumonia was the least common type of IP, found only in 2 patients (4% of cases).

Honeycombing was significantly correlated with FVC%, FEV1%, and FEV1/FVC% (p = 0.013, p = <0.001, p = 0.002 respectively). Also, reticular was significantly correlated with FVC% (p = 0.041).

Conclusion

Semi-quantitative image analysis, including the use of machine learning, provides a great deal of promise in the ILD field; such methods may be used together with visual analysis to obtain the most accurate diagnostic and prognostic information.

Summary/keywords

HRCT is most sensitive in the detection of ILD than chest radiography or conventional chest computed tomography (CT). Advances in HRCT scanning and interpretation have facilitated and improved accuracy for use in diagnosing idiopathic pulmonary fibrosis (IPF), eliminating the need for a surgical biopsy in many patients. Consequently, HRCT scans became sufficient to allow a confident IPF diagnosis

It is important to note that there are potential differences in interpretation of HRCT patterns between thoracic radiologists. However, these differences seem to be in general within a clinically acceptable range of observer variation and can be partially mitigated by review of difficult cases at ILD referral centers.

Semi-quantitative CT assessment is increasingly being used in ILD to identify pulmonary abnormalities and diagnose specific ILDs; recent studies showed that outcomes of computer-assisted imaging can be correlated with lung function tests and degree of dyspnea and functional disability

This study was designed to correlate the degree of parenchymal affection in IP using visual and semi-quantitative HRCT assessment with PFT results. Semi-quantitative imaging, including color-coded images (HU related), is a new and promising approach in the field of ILD diagnosis and prognosis.

Effects of Nintedanib on Quantitative Lung Fibrosis Score in Idiopathic Pulmonary Fibrosis

Background:

Nintedanib slows disease progression in patients with Idiopathic Pulmonary Fibrosis (IPF) by reducing decline in Forced Vital Capacity (FVC). The effects of nintedanib on abnormalities on high-resolution computed tomography scans have not been previously studied.

Objective:

We conducted a Phase IIIb trial to assess the effects of nintedanib on changes in Quantitative Lung Fibrosis (QLF) score and other measures of disease progression in patients with IPF.

Methods:

113 patients were randomized 1:1 to receive nintedanib 150 mg bid or placebo double-blind for ≥6 months, followed by open-label nintedanib. The primary endpoint was the relative change from baseline in QLF score (%) at month 6. Analyses were descriptive and exploratory.

Results:

Adjusted mean relative changes from baseline in QLF score at month 6 were 11.4% in the nintedanib group (n=42) and 14.6% in the placebo group (n=45) (difference 3.2% [95% CI: −9.2, 15.6]). Adjusted mean absolute changes from baseline in QLF score at month 6 were 0.98% and 1.33% in these groups, respectively (difference 0.35% [95% CI: −1.27, 1.96]). Adjusted mean absolute changes from baseline in FVC at month 6 were −14.2 mL and −83.2 mL in the nintedanib (n=54) and placebo (n=54) groups, respectively (difference 69.0 mL [95% CI: −8.7, 146.8]).

Conclusion:

Exploratory data suggest that in patients with IPF, 6 months’ treatment with nintedanib was associated with a numerically smaller degree of fibrotic change in the lungs and reduced FVC decline versus placebo. These data support previous findings that nintedanib slows the progression of IPF.

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.

Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest study

OBJECTIVES

To investigate the intra- and inter-scanner repeatability and reproducibility of CT radiomics features (RF) of fibrosing interstitial lung disease (fILD).

METHODS

For this prospective, IRB-approved test-retest study, CT data of sixty fILD patients were acquired. Group A (n = 30) underwent one repeated CT scan on a single scanner. Group B (n = 30) was scanned using two different CT scanners. All CT data were reconstructed using different reconstruction kernels (soft, intermediate, sharp) and slice thicknesses (one and three millimeters), resulting in twelve datasets per patient. Following ROI placement in fibrotic lung tissue, 86 RF were extracted. Intra- and inter-scanner RF repeatability and reproducibility were assessed by calculating intraclass correlation coefficients (ICCs) for corresponding kernels and slice thicknesses, and between lung-specific and non-lung-specific reconstruction parameters. Furthermore, test-retest lung volumes were compared.

RESULTS

Test-retest demonstrated a majority of RF is highly repeatable for all reconstruction parameter combinations. Intra-scanner reproducibility was negatively affected by reconstruction kernel changes, and further reduced by slice thickness alterations. Inter-scanner reproducibility was highly variable, reconstruction parameter-specific, and greatest if either soft kernels and three-millimeter slice thickness, or lung-specific reconstruction parameters were used for both scans. Test-retest lung volumes showed no significant difference.

CONCLUSION

CT RF of fILD are highly repeatable for constant reconstruction parameters in a single scanner. Intra- and inter-scanner reproducibility are severely impacted by alterations in slice thickness more than reconstruction kernel, and are reconstruction parameter-specific. These findings may facilitate CT data and RF selection and assessment in future fILD radiomics studies collecting data across scanners.

Multimodality imaging in connective tissue disease-related interstitial lung disease

Interstitial lung disease is a well-recognised manifestation and a major cause of morbidity and mortality in patients with connective tissue diseases. Interstitial lung disease may arise in the context of an established connective tissue disease or be the initial manifestation of an otherwise occult autoimmune disorder. Early detection and characterisation are paramount for adequate patient management and require a multidisciplinary approach, in which imaging plays a vital role. Computed tomography is currently the imaging method of choice; however, other imaging techniques have recently been investigated, namely ultrasound, magnetic resonance imaging, and positron-emission tomography, with promising results. The aim of this review is to describe the imaging findings of connective tissue disease-related interstitial lung disease and explain the role of each imaging technique in diagnosis and disease characterisation.

Interstitial lung abnormalities detected incidentally on CT: a Position Paper from the Fleischner Society

The term interstitial lung abnormalities refers to specific CT findings that are potentially compatible with interstitial lung disease in patients without clinical suspicion of the disease. Interstitial lung abnormalities are increasingly recognised as a common feature on CT of the lung in older individuals, occurring in 4-9% of smokers and 2-7% of non-smokers. Identification of interstitial lung abnormalities will increase with implementation of lung cancer screening, along with increased use of CT for other diagnostic purposes. These abnormalities are associated with radiological progression, increased mortality, and the risk of complications from medical interventions, such as chemotherapy and surgery. Management requires distinguishing interstitial lung abnormalities that represent clinically significant interstitial lung disease from those that are subclinical. In particular, it is important to identify the subpleural fibrotic subtype, which is more likely to progress and to be associated with mortality. This multidisciplinary Position Paper by the Fleischner Society addresses important issues regarding interstitial lung abnormalities, including standardisation of the definition and terminology; predisposing risk factors; clinical outcomes; options for initial evaluation, monitoring, and management; the role of quantitative evaluation; and future research needs.

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

Background

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

Methods

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

Results

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

Conclusions

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

Stratification of long-term outcome in stable idiopathic pulmonary fibrosis by combining longitudinal computed tomography and forced vital capacity

OBJECTIVES

To test HRCT with either visual or quantitative analysis in both short-term and long-term follow-up of stable IPF against long-term (transplant-free) survival, beyond 2 years of disease stability.

METHODS

Fifty-eight IPF patients had FVC measurements and HRCTs at baseline (HRCT0), 10-14 months (HRCT1) and 22-26 months (HRCT2). Visual scoring, CALIPER quantitative analysis of HRCT measures, and their deltas were evaluated against combined all-cause mortality and lung transplantation by adjusted Cox proportional hazard models at each time interval.

RESULTS

At HRCT1, a ≥ 20% relative increase in CALIPER-total lung fibrosis yielded the highest radiological association with outcome (C-statistic 0.62). Moreover, the model combining FVC% drop ≥ 10% and ≥ 20% relative increase of CALIPER-total lung fibrosis improved the stratification of outcome (C-statistic 0.69, high-risk category HR 12.1; landmark analysis at HRCT1 C-statistic 0.66, HR 14.9 and at HRCT2 C-statistic 0.61, HR 21.8). Likewise, at HRCT2, the model combining FVC% decrease trend and ≥ 20% relative increase of CALIPER-pulmonary vessel-related volume (VRS) improved the stratification of outcome (C-statistic 0.65, HR 11.0; landmark analysis at HRCT1 C-statistic 0.62, HR 13.8 and at HRCT2 C-statistic 0.58, HR 12.6). A less robust stratification of outcome distinction was also demonstrated with the categorical visual scoring of disease change.

CONCLUSIONS

Annual combined CALIPER -FVC changes showed the greatest stratification of long-term outcome in stable IPF patients, beyond 2 years.

KEY POINTS

• Longitudinal high-resolution computed tomography (HRCT) data is more helpful than baseline HRCT alone for stratification of long-term outcome in IPF. • HRCT changes by visual or quantitative analysis can be added with benefit to the current spirometric reference standard to improve stratification of long-term outcome in IPF. • HRCT follow-up at 12-14 months is more helpful than HRCT follow-up at 23-26 months in clinically stable subjects with IPF.

Automated Computed Tomography analysis in the assessment of Idiopathic Pulmonary Fibrosis severity and progression

PURPOSE

To investigate the role of a quantitative analysis software (CALIPER) in identifying HRCT thresholds predicting IPF patients' survival and lung function decline and its role in detecting changes of HRCT abnormalities related to treatment and their correlation with Forced Vital Capacity (FVC).

METHODS

This retrospective study included 105 patients with a multidisciplinary diagnosis of IPF for whom one HRCT at baseline and concomitant FVC were available. HRCTs were evaluated with CALIPER and the correlation between FVC and radiological features were assessed. Radiological thresholds for survival prediction and functional decline were calculated for all patients. Fifty-nine patients with at least 2 serial HRCTs were classified into two groups based on treatment. For patients for whom a FVC within 3 months of the HRCT was available (n = 44), the correlation of radiological and clinical progression was evaluated.

RESULTS

The correlation between FVC and CALIPER-derived features at baseline was significant and strong. A baseline CALIPER-derived interstitial lung disease (ILD%) extent higher than 20 % and pulmonary vascular related structures (PVRS%) score greater than 5 % defined a worse prognosis. A significant progression of CALIPER-derived features in all patients was found with a faster increase in untreated patients. ILD% and PVRS% changes during follow-up demonstrated strong correlations with FVC changes.

CONCLUSIONS

CALIPER quantification of fibrosis and vascular involvement could distinguish disease progression in treated versus untreated patients and predict the survival. The changes in CALIPER-derived variables over time were significantly correlated to changes in FVC.

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.

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

Background

Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.

Methods

This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.

Results

Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).

Conclusions

TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Electronic supplementary material

The online version of this article (10.1186/s41747-019-0121-6) contains supplementary material, which is available to authorized users.

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.

An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV₁) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

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 using functional imaging is superior in describing disease progression in idiopathic pulmonary fibrosis compared to forced vital capacity

Background

Idiopathic pulmonary fibrosis (IPF) is chronic fibrosing pneumonia with an unpredictable natural disease history. Functional respiratory imaging (FRI) has potential to better characterize this disease. The aim of this study was to identify FRI parameters, which predict FVC decline in patients with IPF.

Methods

An IPF-cohort (treated with pamrevlumab for 48 weeks) was retrospectively studied using FRI. Serial CT’s were compared from 66 subjects. Post-hoc analysis was performed using FRI, FVC and mixed effects models.

Results

Lung volumes, determined by FRI, correlated with FVC (lower lung volumes with lower FVC) (R² = 0.61, p < 0.001). A negative correlation was observed between specific image based airway radius (siRADaw) at total lung capacity (TLC) and FVC (R² = 0.18, p < 0.001). Changes in FVC correlated significantly with changes in lung volumes (R² = 0.18, p < 0.001) and siRADaw (R² = 0.15, p = 0.002) at week 24 and 48, with siRADaw being more sensitive to change than FVC. Loss in lobe volumes (R² = 0.33, p < 0.001), increasing fibrotic tissue (R² = 0.33, p < 0.001) and airway radius (R² = 0.28, p < 0.001) at TLC correlated with changes in FVC but these changes already occur in the lower lobes when FVC is still considered normal.

Conclusion

This study indicates that FRI is a superior tool than FVC in capturing of early and clinically relevant, disease progression in a regional manner.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0918-5) contains supplementary material, which is available to authorized users.

Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis

RATIONALE

Quantitative computed tomographic (CT) measures of baseline disease severity might identify patients with idiopathic pulmonary fibrosis (IPF) with an increased mortality risk. We evaluated whether quantitative CT variables could act as a cohort enrichment tool in future IPF drug trials.

OBJECTIVES

To determine whether computer-derived CT measures, specifically measures of pulmonary vessel-related structures (VRSs), can better predict functional decline and survival in IPF and reduce requisite sample sizes in drug trial populations.

METHODS

Patients with IPF undergoing volumetric noncontrast CT imaging at the Royal Brompton Hospital, London, and St. Antonius Hospital, Utrecht, were examined to identify pulmonary function measures (including FVC) and visual and computer-derived (CALIPER [Computer-Aided Lung Informatics for Pathology Evaluation and Rating] software) CT features predictive of mortality and FVC decline. The discovery cohort comprised 247 consecutive patients, with validation of results conducted in a separate cohort of 284 patients, all fulfilling drug trial entry criteria.

MEASUREMENTS AND MAIN RESULTS

In the discovery and validation cohorts, CALIPER-derived features, particularly VRS scores, were among the strongest predictors of survival and FVC decline. CALIPER results were accentuated in patients with less extensive disease, outperforming pulmonary function measures. When used as a cohort enrichment tool, a CALIPER VRS score greater than 4.4% of the lung was able to reduce the requisite sample size of an IPF drug trial by 26%.

CONCLUSIONS

Our study has validated a new quantitative CT measure in patients with IPF fulfilling drug trial entry criteria-the VRS score-that outperformed current gold standard measures of outcome. When used for cohort enrichment in an IPF drug trial setting, VRS threshold scores can reduce a required IPF drug trial population size by 25%, thereby limiting prohibitive trial costs. Importantly, VRS scores identify patients in whom antifibrotic medication prolongs life and reduces FVC decline.

Effects of emphysema on physiological and prognostic characteristics of lung function in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Combined pulmonary fibrosis and emphysema (CPFE) is characterized by preserved lung volume and slower lung function decline. However, it is unclear at what extent emphysema begins to impact respiratory physiology and prognostic characteristics in idiopathic pulmonary fibrosis (IPF). We estimated the extent of emphysema that could be used to define CPFE in IPF.

METHODS

The extent of emphysema was observed on high-resolution computed tomography scans and measured by a texture-based automated quantification system in 209 IPF patients. We analysed the impact of differences in the extent of emphysema on the annual decline rate and prognostic significance of lung function parameters.

RESULTS

The extent of emphysema was ≥5% in 53 patients (25%), ≥10% in 23 patients (11%) and ≥15% in 12 patients (6%). Patients with emphysema to an extent of ≥5% were more frequently men and ever-smokers; they had more preserved lung volume and lower forced vital capacity (FVC) decline rates than those with no or trivial emphysema. The FVC decline rate was a significant predictor of mortality in patients with no or trivial emphysema (hazard ratio (HR): 0.933, P < 0.001) and in patients with an extent of emphysema ≥5% (HR: 0.906, P < 0.001). However, diffusing capacity of the lung for carbon monoxide (DL_CO ) was the most significant prognostic factor in those patients with an extent of emphysema ≥10% (HR: 0.972, P = 0.040) and ≥15% (HR: 0.942, P = 0.023). A 10% cut-off value for the extent of emphysema created the most significant difference in the annual FVC decline rate in IPF patients.

CONCLUSION

In IPF, emphysema to an extent of ≥10% affects both the annual decline rate and the prognostic significance of FVC. This extent could be used to define CPFE.

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

We evaluated performance characteristics and estimated the minimal clinically important difference (MCID) of data-driven texture analysis (DTA), a high-resolution computed tomography (HRCT)-derived measurement of lung fibrosis, in subjects with idiopathic pulmonary fibrosis (IPF).

The study population included 141 subjects with IPF from two interventional clinical trials who had both baseline and nominal 54- or 60-week follow-up HRCT. DTA scores were computed and compared with forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide, distance covered during a 6-min walk test and St George's Respiratory Questionnaire scores to assess the method's reliability, validity and responsiveness. Anchor- and distribution-based methods were used to estimate its MCID.

DTA had acceptable reliability in subjects appearing stable according to anchor variables at follow-up. Correlations between the DTA score and other clinical measurements at baseline were moderate to weak and in the hypothesised directions. Acceptable responsiveness was demonstrated by moderate to weak correlations (in the directions hypothesised) between changes in the DTA score and changes in other parameters. Using FVC as an anchor, MCID was estimated to be 3.4%.

Quantification of lung fibrosis extent on HRCT using DTA is reliable, valid and responsive, and an increase of ∼3.4% represents a clinically important change.

Investigating the effects of nintedanib on biomarkers of extracellular matrix turnover in patients with IPF: design of the randomised placebo-controlled INMARK®trial

Introduction

A feature of the pathogenesis of idiopathic pulmonary fibrosis (IPF) is the excess accumulation of extracellular matrix (ECM) in the lungs. Cleavage of the ECM by metalloproteinases (MMPs) generates free-circulating protein fragments known as neoepitopes. The PROFILE study suggested that changes in ECM turnover proteins may be of value as markers of disease progression in patients with IPF. Nintedanib is an approved treatment for IPF that slows disease progression by reducing decline in forced vital capacity (FVC).

Methods and analysis

The INMARK® trial is evaluating the effect of nintedanib on the rates of change of biomarkers of ECM turnover in patients with IPF, the value of changes in these biomarkers as predictors of disease progression and whether nintedanib affects the associations between changes in these biomarkers and disease progression. Following a screening period, 347 patients with IPF and FVC ≥80% predicted were randomised 1:2 to receive nintedanib 150 mg two times a day or placebo for 12 weeks, followed by an open-label period in which all patients will receive nintedanib for 40 weeks. The primary endpoint is the rate of change in C reactive protein degraded by MMP-1/8 from baseline to week 12.

Ethics and dissemination

This trial is being conducted in compliance with the protocol, the ethical principles detailed in the Declaration of Helsinki and in accordance with the International Conference on Harmonisation Harmonised Tripartite Guideline for Good Clinical Practice. The results of the trial will be presented at national and international meetings and published in peer-reviewed journals.

Trial registration number

NCT02788474.

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.