CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a Position Paper from the Fleischner society

Radiomics on slice-reduced versus full-chest computed tomography for diagnosis and staging of interstitial lung disease in systemic sclerosis: A comparative analysis

Purpose

The purpose of this study was to evaluate the efficacy of radiomics derived from slice-reduced CT (srCT) scans versus full-chest CT (fcCT) for diagnosing and staging of interstitial lung disease (ILD) in systemic sclerosis (SSc), considering the potential to reduce radiation exposure.

Material and methods

The fcCT corresponded to a standard high-resolution full-chest CT whereas the srCT consisted of nine axial slices. 1451 radiomic features in two dimensions from srCT and 1375 features in three dimensions from fcCT scans were extracted from 166 SSc patients. The study included first- and second-order features from original and wavelet-transformed images. We assessed the predictive performance of quantitative CT (qCT)-based logistic regression (LR) models relying on preselected features and machine learning workflows involving LR and extra-trees classifiers with data-driven feature selection. The area under the receiver operating characteristic curve (AUC) was used to estimate model performance.

Results

The best models for diagnosis and staging ILD achieved AUC=0.85±0.08 and AUC=0.82±0.08 with srCT, and AUC=0.83±0.06 and AUC=0.76±0.08 with fcCT, respectively. srCT-based models showed slightly superior performance over fcCT-based models, particularly in 2D-radiomic analyses when interpolation resolution closely matched the original in-plane resolution. For diagnosis, the LR outperformed qCT-models, whereas for staging, the best results were obtained with a qCT-based model.

Conclusions

Radiomics from srCT is an effective and preferable alternative to fcCT for diagnosing and staging SSc-ILD. This approach not only enhances predictive accuracy but also minimizes radiation exposure risks, offering a promising avenue for improved treatment decision support in SSc-ILD management.

Effect of antigen removal in hypersensitivity pneumonitis

BACKGROUND

Antigen removal is a cornerstone of treatment of hypersensitivity pneumonitis (HP), but its association with transplant-free survival remains unclear. Further, HP guidelines conflict as to whether antigen removal is a recommended diagnostic test in patients with suspected HP.

OBJECTIVE

The purpose of this study is to (1) evaluate the impact of antigen removal on transplant-free survival and (2) to describe the impact of antigen removal on pulmonary function testing and imaging in a retrospective cohort of patients with HP.

METHODS

We retrospectively identified HP patients evaluated between 2011 and 2020. Demographic, physiologic, radiographic, and pathologic data were recorded.

RESULTS

212 patients were included in the cohort. Patients who identified and removed antigen had a better transplant-free survival than patients who did not identify antigen and patients who identified but did not remove antigen. Antigen removal was associated with improvement in FVC by 10% predicted in 16.9% of patients with fibrotic HP and 56.7% of patients with nonfibrotic HP.

DISCUSSION

Our results suggest that over 50% of nonfibrotic HP patients and 16.9% of fibrotic HP patients improve with exposure removal. In addition, antigen removal, rather than antigen identification, is associated with transplant-free survival in HP.

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.

[68Ga]FAPI PET for Imaging and Treatment Monitoring in a Preclinical Model of Pulmonary Fibrosis: Comparison to [18F]FDG PET and CT

PURPOSE

This study aimed to evaluate the feasibility of using [68Ga]-fibroblast-activating protein inhibitor (FAPI) positron emission tomography (PET) imaging for diagnosing pulmonary fibrosis in a mouse model. We also examined its value in monitoring treatment response and compared it with traditional [18F]-fluorodeoxyglucose (FDG) PET and computed tomography (CT) imaging.

METHODS

A model of idiopathic pulmonary fibrosis was established using intratracheal injection of bleomycin (BLM, 2 mg/kg) into C57BL/6 male mice. For the treatment of IPF, a daily oral dose of 400 mg/kg/day of pirfenidone was administered from 9 to 28 days after the establishment of the model. Disease progression and treatment efficacy were assessed at different stages of the disease every week for four weeks using CT, [18F]FDG PET, and [68Ga]FAPI PET (baseline imaging performed at week 0). Mice were sacrificed and lung tissues were harvested for hematoxylin-eosin staining, picrosirius red staining, and immunohistochemical staining for glucose transporter 1 (GLUT1) and FAP. Expression levels of GLUT1 and FAP in pathological sections were quantified. Correlations between imaging parameters and pathological quantitative values were analyzed.

RESULTS

CT, [18F]FDG PET and [68Ga]FAPI PET revealed anatomical and functional changes in the lung that reflected progression of pulmonary fibrosis. In untreated mice with pulmonary fibrosis, lung uptake of [18F]FDG peaked on day 14, while [68Ga]FAPI uptake and mean lung density peaked on day 21. In mice treated with pirfenidone, mean lung density and lung uptake of both PET tracers decreased. Mean lung density, [18F]FDG uptake, and [68Ga]FAPI uptake correlated well with quantitative values of picrosirius red staining, GLUT1 expression, and FAP expression, respectively. Conclusions: Although traditional CT and [18F]FDG PET reflect anatomical and metabolic status in fibrotic lung, [68Ga]FAPI PET provides a means of evaluating fibrosis progression and monitoring treatment response.

Diagnostic Value of Krebs von den Lungen (KL-6) for Interstitial Lung Disease: A European Prospective Cohort

INTRODUCTION

Krebs von den Lungen 6 (KL-6) is a mucin-1 glycoprotein produced by type II pneumocytes. High levels of KL-6 in blood may be found in patients with lung fibrosis. In Asia this biomarker is used for diagnosis and prognosis in interstitial lung diseases (ILD). There is a lack of information regarding KL-6 cut-off point for diagnosis and prognosis in European population. The aim of this study was to establish the cut-off point for serum KL-6 associated with the presence of ILD in the Spanish population.

METHODS

Prospective study including subjects who underwent chest HRCT, PFTs and autoimmune blood analysis. Two groups were created: non-ILD subjects and ILD patients. Serum KL-6 concentrations were measured using a Lumipulse KL-6 reagent assay and the optimal cut-off value was evaluated by a ROC analysis. Data on demographics and smoking history was also collected.

RESULTS

One hundred seventy-nine patients were included, 102 with ILD. Median serum KL-6 values overall were 762U/mL, 1080 (±787)U/mL for the ILD group vs 340 (±152)U/mL for the non-ILD group (p<0.0001). The main radiological pattern was NSIP (43%). ROC analysis showed greater specificity (86%) and sensitivity (82%) for KL-6 465U/mL for detecting ILD patients. The multivariate logistic regression model pointed to the male sex, higher KL-6 values, lower FVC and low DLCO values as independent factors associated with ILD.

CONCLUSION

Serum KL-6 values greater than 465U/mL have excellent sensitivity and specificity for detecting ILD in our Spanish cohort. Multicentre studies are needed to validate our results.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

BACKGROUND

Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis.

METHODS

CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Correlation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progression rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test.

RESULTS

The follow-up was 3.8 ± 1.5 years encompassing 105 CT scans, with 3.3 ± 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, ρ = -0.30 [95% CI: -0.16 to -0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplantations. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR ≥ 4%/year during the first two years had a poorer prognosis (p = 0.001).

CONCLUSIONS

Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome.

A validation of register-derived diagnoses of interstitial lung disease in patients with inflammatory arthritis: data from the NOR-DMARD study

OBJECTIVE

There is a lack of knowledge concerning the validity of the interstitial lung disease (ILD) diagnoses used in epidemiological studies on rheumatic diseases. This paper seeks to verify register-derived ILD diagnoses using chest computed tomography (CT) and medical records as a gold standard.

METHOD

The Norwegian Anti-Rheumatic Drug Register (NOR-DMARD) is a multicentre prospective observational study of patients with inflammatory arthritis who start treatment with disease-modifying anti-rheumatic drugs. NOR-DMARD is linked to the Norwegian Patient Registry (NPR) and Cause of Death Registry. We searched registers for ILD coded by ICD-10 J84 or J99 among patients with rheumatoid arthritis, psoriatic arthritis, or spondyloarthritis. We extracted chest CT reports and medical records from participating hospitals. Two expert thoracic radiologists scored examinations to confirm the ILD diagnosis. We also searched medical records to find justifications for the diagnosis following multidisciplinary evaluations. We calculated the positive predictive values (PPVs) for ILD across subsets.

RESULTS

We identified 71 cases with an ILD diagnosis. CT examinations were available in 65/71 patients (91.5%), of whom ILD was confirmed on CT in 29/65 (44.6%). In a further 10 patients, medical records confirmed the diagnosis, giving a total of 39/71 verified cases. The PPV of a register-derived ILD diagnosis was thus 54.9%. In a subset of patients who had received an ILD code at two or more time-points and had a CT scan taken within a relevant period, the PPV was 72.2%.

CONCLUSION

The validity of register-based diagnoses of ILD must be carefully considered in epidemiological studies.

Measurement Variability of Same-Day CT Quantification of Interstitial Lung Disease: A Multicenter Prospective Study

Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes. Deep learning-based texture analysis software was used to segment ILD features. Fibrosis extent was defined as the sum of reticular opacity and honeycombing cysts. Measurement variability between scans was assessed with Bland-Altman analyses for absolute and relative differences with 95% limits of agreement (LOA). The contribution of fibrosis extent to variability was analyzed using a multivariable linear mixed-effects model while adjusting for lung volume. Eight readers assessed ILD fibrosis stability with and without QCT information for 30 randomly selected samples. Results Sixty-five participants were enrolled in this study (mean age, 68.7 years ± 10 [SD]; 47 [72%] men, 18 [28%] women). Between two same-day CT scans, the 95% LOA for the mean absolute and relative differences of quantitative fibrosis extent were -0.9% to 1.0% and -14.8% to 16.1%, respectively. However, these variabilities increased to 95% LOA of -11.3% to 3.9% and -123.1% to 18.4% between CT scans with different reconstruction parameters. Multivariable analysis showed that absolute differences were not associated with the baseline extent of fibrosis (P = .09), but the relative differences were negatively associated (β = -0.252, P < .001). The QCT results increased readers' specificity in interpreting ILD fibrosis stability (91.7% vs 94.6%, P = .02). Conclusion The absolute QCT measurement variability of fibrosis extent in ILD was 1% in same-day CT scans. Keywords: CT, CT-Quantitative, Thorax, Lung, Lung Diseases, Interstitial, Pulmonary Fibrosis, Diagnosis, Computer Assisted, Diagnostic Imaging Supplemental material is available for this article. © RSNA, 2024.

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

RATIONALE AND OBJECTIVES

The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels.

MATERIALS AND METHODS

Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method.

RESULTS

A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA [-0.4, 0.4]).

CONCLUSION

Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.

FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

Rationale and introduction

It is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.

Materials and methods

Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.

Results

A total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827-0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816-0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720-0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.

Conclusion

The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

Radiographic grading system for usual interstitial pneumonia correlates with mortality and may serve as a surrogate endpoint in clinical trials

PURPOSE

Usual interstitial pneumonia (UIP)/ idiopathic pulmonary fibrosis (IFP) is a relentlessly progressive lung disease with outcomes similar to cancer. We have previously established a radiologic grading system for UIP and demonstrated that it correlates with pulmonary function tests; here we test the hypothesis that it correlates with mortality. Validating a correlation with mortality will demonstrate the utility of this system for monitoring progression over time clinically and in trials of anti-fibrotic agents.

METHODS

We searched the radiology record system "Catalyst" to identify cases and reviewed each case to confirm the diagnosis. 94 patients met the inclusion criteria for further assessment. Chest CT grade was determined by consensus of two cardiothoracic radiologists. Data was analyzed to identify the interval between chest CT and death. This interval was correlated with CT grade using Spearman correlation analysis.

RESULTS

For all cases, chest CT grade and mortality demonstrated a positive correlation of rs = 0.37732, 2-tailed p = 0.00018. We also employed subgroup analysis; for the subgroup with intervals less than or equal to 100 days, there was a positive correlation, rs = 0.48339, 2-tailed p = 0.03602; for the subgroup with an interval greater than 100 days between imaging and death there was a positive correlation, rs = 0.302, 2-tailed p = 0.00846.

CONCLUSION

These data support use of this system for monitoring clinical progression and as a surrogate endpoint for clinical trials. Future work building upon the data presented here will evaluate its utility in clinical trials and develop automated grading.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

CLINICAL RELEVANCE STATEMENT

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

KEY POINTS

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

Are cleaning activities a source of exposure to crystalline silica in women with rheumatoid arthritis? A case-control study

INTRODUCTION

Inhalation of crystalline silica (silicon dioxide, SiO2) is associated with a wide range of acute and chronic diseases, including rheumatoid arthritis (RA). The objectives of this work were to identify the main sources of exposure to SiO2 in a series of patients with RA not selected on the basis of their professional activity, compared with a representative sample of the French general population, and to assess the association between silica exposure and disease features.

METHODS

The Dust Exposure Life-Course Questionnaire (DELCQ) is a tool that enables retrospective quantification of both occupational and non-occupational lifetime exposure to SiO2. DELCQ-previously validated in a large representative sample of the French general population-was administered to 97 consecutive RA patients, and exposure scores were compared between cases and age, gender and smoking status-matched controls (1:4). The main sources of SiO2 exposure were identified in cases and controls, and source-specific exposure levels were compared. The association between DELCQ scores and disease variables in cases was tested via univariable and multivariable analyses.

RESULTS

In women with RA, the main sources of SiO2 exposure were cleaning activities and dusty clothes laundry, with higher exposure levels from these sources versus the general population (p<0.005). Across the whole series of RA patients, high SiO2 exposure was independently associated with mediastinal lymphadenopathy (OR 6.3, 95% CI 1.4 to 27.7).

CONCLUSION

Cleaning activities and dusty clothes laundry may be underestimated sources of SiO2 exposure in women with RA.

Histologic Analysis of Idiopathic Pulmonary Fibrosis by Morphometric and Fractal Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disorder, ultimately leading to respiratory failure and death. Despite great research advances in understanding the mechanisms underlying the disease, its diagnosis, and its treatment, IPF still remains idiopathic without known biological or histological markers able to predict disease progression or response to treatment. The histologic hallmark of IPF is usual interstitial pneumonia (UIP), with its intricate architectural distortion and temporal inhomogeneity. We hypothesize that normal lung alveolar architecture can be compared to fractals, such as the Pythagoras tree with its fractal dimension (D_f), and every pathological insult, distorting the normal lung structure, could result in D_f variations. In this study, we aimed to assess the UIP histologic fractal dimension in relationship to other morphometric parameters in newly diagnosed IPF patients and its possible role in the prognostic stratification of the disease. Clinical data and lung tissue specimens were obtained from twelve patients with IPF, twelve patients with non-specific interstitial pneumonia (NSIP), and age-matched "healthy" control lung tissue from patients undergoing lung surgery for other causes. Histology and histomorphometry were performed to evaluate D_f and lacunarity measures, using the box counting method on the FracLac ImageJ plugin. The results showed that D_f was significantly higher in IPF patients compared to controls and fibrotic NSIP patients, indicating greater architectural distortion in IPF. Additionally, high D_f values were associated with higher fibroblastic foci density and worse prognostic outcomes in IPF, suggesting that D_f may serve as a potential novel prognostic marker for IPF. The scalability of D_f measurements was demonstrated through repeated measurements on smaller portions from the same surgical biopsies, which were selected to mimic a cryobiopsy. Our study provides further evidence to support the use of fractal morphometry as a tool for quantifying and determining lung tissue remodeling in IPF, and we demonstrated a significant correlation between histological and radiological D_f in UIP pattern, as well as a significant association between D_f and FF density. Furthermore, our study demonstrates the scalability and self-similarity of D_f measurements across different biopsy types, including surgical and smaller specimens.

Unmet needs and perspectives in rheumatoid arthritis-associated interstitial lung disease: A critical review

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by synovitis as the most common clinical manifestation, and interstitial lung disease (RA-ILD) represents one of the most common and potentially severe extra-articular features. Our current understanding of the mechanisms and predictors of RA-ILD is limited despite the demonstration that an early identification of progressive fibrosing forms is crucial to provide timely treatment with antifibrotic therapies. While high resolution computed tomography is the gold standard technique for the diagnosis and follow-up of RA-ILD, it has been hypothesized that serum biomarkers (including novel and rare autoantibodies), new imaging techniques such as ultrasound of the lung, or the application of innovative radiologic algorithms may help towards predicting and detecting early forms of diseases. Further, while new treatments are becoming available for idiopathic and connective tissue disease-associated forms of lung fibrosis, the treatment of RA-ILD remains anecdotal and largely unexplored. We are convinced that a better understanding of the mechanisms connecting RA with ILD in a subgroup of patients as well as the creation of adequate diagnostic pathways will be mandatory steps for a more effective management of this clinically challenging entity.

Imaging Fibrosis

Tissue injury in nonmalignant human disease can develop from either disproportionate inflammation or exaggerated fibrotic responses. The molecular and cellular fundamental of these 2 processes, their impact on disease prognosis and the treatment concept deviates fundamentally. Consequently, the synchronous assessment and quantification of these 2 processes in vivo is extremely desirable. Although noninvasive molecular techniques such as ¹⁸F-fluorodeoxyglucose PET offer insights into the degree of inflammatory activity, the assessment of the molecular dynamics of fibrosis remains challenging. The ⁶⁸Ga-fibroblast activation protein inhibitor-46 may improve noninvasive clinical diagnostic performance in patients with both fibroinflammatory pathology and long-term CT-abnormalities after severe COVID-19.

Physiological underpinnings of exertional dyspnoea in mild fibrosing interstitial lung disease

The functional disturbances driving "out-of-proportion" dyspnoea in patients with fibrosing interstitial lung disease (f-ILD) showing only mild restrictive abnormalities remain poorly understood. Eighteen patients (10 with idiopathic pulmonary fibrosis) showing preserved spirometry and mildly reduced total lung capacity (≥70% predicted) and 18 controls underwent an incremental cardiopulmonary exercise test with measurements of operating lung volumes and Borg dyspnoea scores. Patients' lower exercise tolerance was associated with higher ventilation (V̇_E)/carbon dioxide (V̇CO₂) compared with controls (V̇_E/V̇CO₂ nadir=35 ± 3 versus 29 ± 2; p < 0.001). Patients showed higher tidal volume/inspiratory capacity and lower inspiratory reserve volume at a given exercise intensity, reporting higher dyspnoea scores as a function of both work rate and V̇_E. Steeper dyspnoea-work rate slopes were associated with lower lung diffusing capacity, higher V̇_E/V̇CO₂, and lower peak O₂ uptake (p < 0.05). Heightened ventilatory demands in the setting of progressively lower capacity for tidal volume expansion on exertion largely explain higher-than-expected dyspnoea in f-ILD patients with largely preserved dynamic and "static" lung volumes at rest.

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.

Unsupervised machine learning identifies predictive progression markers of IPF

OBJECTIVES

To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome.

METHODS

We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center.

RESULTS

Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort.

CONCLUSIONS

Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types.

KEY POINTS

• Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort.

Idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is the most common fibrosing lung disease. It is associated with a very poor prognosis. Treatments can delay the progression of IPF, so early diagnosis is fundamental. Radiologists play a fundamental role in the evaluation and accurate diagnosis of IPF. Identifying the characteristic patterns of IPF on high-resolution computed tomography (HRCT) is key in the process of multidisciplinary diagnosis, often obviating the need for surgical lung biopsies. This review describes and illustrates the clinical and imaging findings in IPF in the context of the most recent international guidelines, as well as the differential diagnosis and the role of HRCT in follow-up and assessment of complications.

Transferability of radiomic signatures from experimental to human interstitial lung disease

Background

Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.

Methods

Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.

Results

Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model’s performance to AUC = 0.912 ± 0.058.

Conclusion

Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.

A Risk Score to Detect Subclinical Rheumatoid Arthritis-Associated Interstitial Lung Disease

OBJECTIVE

Patients at high risk of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) would benefit from being identified before the onset of respiratory symptoms; this can be done by screening patients with the use of chest high-resolution computed tomography (HRCT). Our objective was to develop and validate a risk score for patients who have subclinical RA-ILD.

METHODS

Our study included a discovery population and a replication population from 2 prospective RA cohorts (ESPOIR and TRANSLATE2, respectively) without pulmonary symptoms who had received chest HRCT scans. All patients were genotyped for MUC5B rs35705950. After multiple logistic regression, a risk score based on independent risk factors for subclinical RA-ILD was developed in the discovery population and tested for validation in the replication population.

RESULTS

The discovery population included 163 patients with RA, and the replication population included 89 patients with RA. The prevalence of subclinical RA-ILD was 19.0% and 16.9%, respectively. In the discovery population, independent risk factors for subclinical RA-ILD were presence of the MUC5B rs35705950 T allele (odds ratio [OR] 3.74 [95% confidence interval (95% CI) 1.37, 10.39]), male sex (OR 3.93 [95% CI 1.40, 11.39]), older age at RA onset (for each year, OR 1.10 [95% CI 1.04, 1.16]), and increased mean Disease Activity Score in 28 joints using the erythrocyte sedimentation rate (for each unit, OR 2.03 [95% CI 1.24, 3.42]). We developed and validated a derived risk score with receiver operating characteristic areas under the curve of 0.82 (95% CI 0.70-0.94) for the discovery population and 0.78 (95% CI 0.65-0.92) for the replication population. Excluding MUC5B rs35705950 from the model provided a lower goodness of fit (likelihood ratio test, P = 0.01).

CONCLUSION

We developed and validated a risk score that could help identify patients at high risk of subclinical RA-ILD. Our findings support an important contribution of MUC5B rs35705950 to subclinical RA-ILD risk.

Respiratory function and CT abnormalities among survivors of COVID-19 pneumonia: a nationwide follow-up study

INTRODUCTION

Considering the pulmonary burden caused by acute COVID-19, questions remain of respiratory consequences after recovery. The aim of the study was to describe respiratory function of COVID-19 pneumonia survivors at mid-term follow-up (median 68 days) and assess whether impairments were predicted by acute illness severity or residual CT abnormalities.

METHODS

Residents of Iceland that had COVID-19 and oxygen saturation ≤94% from 28 February 2020 to 30 April 2021 were offered a clinical follow-up visit with an interview, a 6 min walk test (6MWT), spirometry with gas exchange measurement and chest CT. The results of these examinations were described, grouped by the level of care during acute illness. The associations of disease severity and CT abnormalities at follow-up with subjective dyspnoea, 6MWT results and lung function test results were estimated with regression analyses.

RESULTS

Of 190 eligible patients, 164 (86%) participated in the study. Of those, 32 had never been admitted to hospital, 103 were admitted to hospital without intensive care and 29 had required intensive care. At a follow-up, need for intensive care during acute illness was associated with shorter walking distance on 6MWT, lower oxygen saturation and lower DL_CO. Imaging abnormalities at follow-up were observed for most participants (74%) and the magnitude of these changes was associated with decrements in 6MWT distance, oxygen saturation, forced vital capacity and DL_CO.

CONCLUSIONS

The findings show that impaired exercise capacity and lung physiology at follow-up were primarily observed for patients with COVID-19 pneumonia that required intensive care treatment and/or had persistent imaging abnormalities.

Prognostic Factors for Pulmonary Fibrosis Following Pneumonia in Patients with COVID-19: A Prospective Study

The frequency and clinical manifestation of lung fibrosis accompanied by coronavirus disease (COVID-19) are not well-established. We aimed to identify the factors attributed to post-COVID-19 fibrosis. This single-center prospective study included patients diagnosed with COVID-19 pneumonia from 12 April to 22 October 2021 in the Republic of Korea. The primary outcome was the presence of pulmonary fibrosis on a CT scan 3 months after discharge; the fibrosis risk was estimated by a multiple logistic regression. The mean patient age was 55.03 ± 12.32 (range 27–85) years; 65 (66.3%) were men and 33 (33.7%) were women. The age, Charlson Comorbidity Index, lactate dehydrogenase level, aspartate aminotransferase level, and Krebs von den Lungen-6 level were significantly higher and the albumin level and the saturation of the peripheral oxygen/fraction of inspired oxygen (SpO2/FiO2) ratio were significantly lower in the fibrosis group than in the non-fibrosis group; the need for initial oxygen support was also greater in the fibrosis group. An older age (adjusted odds ratio (AOR) 1.12; 95% confidence interval (CI) 1.03–1.21) and a lower initial SpO2/FiO2 ratio (AOR 7.17; 95% CI 1.72–29.91) were significant independent risk factors for pulmonary fibrosis after COVID-19 pneumonia. An older age and a low initial SpO2/FiO2 ratio were crucial in predicting pulmonary fibrosis after COVID-19 pneumonia.

Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD.

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

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

Idiopathic Pulmonary Fibrosis Mortality Risk Prediction Based on Artificial Intelligence: The CTPF Model

Background: Idiopathic pulmonary fibrosis (IPF) needs a precise prediction method for its prognosis. This study took advantage of artificial intelligence (AI) deep learning to develop a new mortality risk prediction model for IPF patients.

Methods: We established an artificial intelligence honeycomb segmentation system that segmented the honeycomb tissue area automatically from 102 manually labeled (by radiologists) cases of IPF patients’ CT images. The percentage of honeycomb in the lung was calculated as the CT fibrosis score (CTS). The severity of the patients was evaluated by pulmonary function and physiological feature (PF) parameters (including FVC%pred, DLco%pred, SpO2%, age, and gender). Another 206 IPF cases were randomly divided into a training set (n = 165) and a verification set (n = 41) to calculate the fibrosis percentage in each case by the AI system mentioned previously. Then, using a competing risk (Fine–Gray) proportional hazards model, a risk score model was created according to the training set’s patient data and used the validation data set to validate this model.

Result: The final risk prediction model (CTPF) was established, and it included the CT stages and the PF (pulmonary function and physiological features) grades. The CT stages were defined into three stages: stage I (CTS≤5), stage II (5 < CTS<25), and stage III (≥25). The PF grades were classified into mild (a, 0–3 points), moderate (b, 4–6 points), and severe (c, 7–10 points). The AUC index and Briers scores at 1, 2, and 3 years in the training set were as follows: 74.3 [63.2,85.4], 8.6 [2.4,14.8]; 78 [70.2,85.9], 16.0 [10.1,22.0]; and 72.8 [58.3,87.3], 18.2 [11.9,24.6]. The results of the validation sets were similar and suggested that high-risk patients had significantly higher mortality rates.

Conclusion: This CTPF model with AI technology can predict mortality risk in IPF precisely.

Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline

Background: This American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana de Tórax guideline updates prior idiopathic pulmonary fibrosis (IPF) guidelines and addresses the progression of pulmonary fibrosis in patients with interstitial lung diseases (ILDs) other than IPF. Methods: A committee was composed of multidisciplinary experts in ILD, methodologists, and patient representatives. 1) Update of IPF: Radiological and histopathological criteria for IPF were updated by consensus. Questions about transbronchial lung cryobiopsy, genomic classifier testing, antacid medication, and antireflux surgery were informed by systematic reviews and answered with evidence-based recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. 2) Progressive pulmonary fibrosis (PPF): PPF was defined, and then radiological and physiological criteria for PPF were determined by consensus. Questions about pirfenidone and nintedanib were informed by systematic reviews and answered with evidence-based recommendations using the GRADE approach. Results:1) Update of IPF: A conditional recommendation was made to regard transbronchial lung cryobiopsy as an acceptable alternative to surgical lung biopsy in centers with appropriate expertise. No recommendation was made for or against genomic classifier testing. Conditional recommendations were made against antacid medication and antireflux surgery for the treatment of IPF. 2) PPF: PPF was defined as at least two of three criteria (worsening symptoms, radiological progression, and physiological progression) occurring within the past year with no alternative explanation in a patient with an ILD other than IPF. A conditional recommendation was made for nintedanib, and additional research into pirfenidone was recommended. Conclusions: The conditional recommendations in this guideline are intended to provide the basis for rational, informed decisions by clinicians.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19

BACKGROUND

Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

METHODS

Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

RESULTS

Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

CONCLUSIONS

Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19

BACKGROUND

Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

METHODS

Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

RESULTS

Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

CONCLUSIONS

Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Serial decline in lung volume parameters on computed tomography (CT) predicts outcome in idiopathic pulmonary fibrosis (IPF)

OBJECTIVES

In patients with IPF, this study aimed (i) to examine the relationship between serial change in CT parameters of lung volume and lung function, (ii) to identify the prognostic value of serial change in CT parameters of lung volume, and (iii) to define a threshold for serial change in CT markers of lung volume that optimally captures disease progression.

METHODS

Serial CTs were analysed for progressive volume loss or fibrosis progression in 81 IPF patients (66 males, median age = 67 years) with concurrent forced vital capacity (FVC) (median follow-up 12 months, range 6-23 months). Serial CT measurements of volume loss comprised oblique fissure posterior retraction distance (OFPRD), aortosternal distance (ASD), lung height corrected for body habitus (LH), and automated CT-derived total lung volumes (ALV) (measured using commercially available software). Fibrosis progression was scored visually. Serial changes in CT markers and FVC were compared using regression analysis, and evaluated against mortality using Cox proportional hazards.

RESULTS

There were 58 deaths (72%, median survival = 17 months). Annual % change in ALV was most significantly related to annual % change in FVC (R2 = 0.26, p < 0.0001). On multivariate analysis, annual % change in ASD predicted mortality (HR = 0.97, p < 0.001), whereas change in FVC did not. A 25% decline in annual % change in ASD best predicted mortality, superior to 10% decline in FVC and fibrosis progression.

CONCLUSION

In IPF, serial decline in CT markers of lung volume and, specifically, annualised 25% reduction in aortosternal distance provides evidence of disease progression, not always identified by FVC trends or changes in fibrosis extent.

KEY POINTS

• Serial decline in automated and surrogate markers of lung volume on CT corresponds to changes in FVC. • Annualised reductions in the distance between ascending aorta and posterior border of the sternum on CT predict mortality beyond annualised percentage change in FVC.

A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications

Post-acute sequelae of COVID (PASC), usually referred to as 'Long COVID' (a phenotype of COVID-19), is a relatively frequent consequence of SARS-CoV-2 infection, in which symptoms such as breathlessness, fatigue, 'brain fog', tissue damage, inflammation, and coagulopathies (dysfunctions of the blood coagulation system) persist long after the initial infection. It bears similarities to other post-viral syndromes, and to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Many regulatory health bodies still do not recognize this syndrome as a separate disease entity, and refer to it under the broad terminology of 'COVID', although its demographics are quite different from those of acute COVID-19. A few years ago, we discovered that fibrinogen in blood can clot into an anomalous 'amyloid' form of fibrin that (like other β-rich amyloids and prions) is relatively resistant to proteolysis (fibrinolysis). The result, as is strongly manifested in platelet-poor plasma (PPP) of individuals with Long COVID, is extensive fibrin amyloid microclots that can persist, can entrap other proteins, and that may lead to the production of various autoantibodies. These microclots are more-or-less easily measured in PPP with the stain thioflavin T and a simple fluorescence microscope. Although the symptoms of Long COVID are multifarious, we here argue that the ability of these fibrin amyloid microclots (fibrinaloids) to block up capillaries, and thus to limit the passage of red blood cells and hence O2 exchange, can actually underpin the majority of these symptoms. Consistent with this, in a preliminary report, it has been shown that suitable and closely monitored 'triple' anticoagulant therapy that leads to the removal of the microclots also removes the other symptoms. Fibrin amyloid microclots represent a novel and potentially important target for both the understanding and treatment of Long COVID and related disorders.

Usefulness of Body Composition CT Analysis in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

PURPOSE

To evaluate the feasibility of a chest CT-based body composition analysis in idiopathic pulmonary fibrosis (IPF), and to investigate the respective contribution of lung and muscle CT quantitative analyses to the prognosis of IPF.

METHOD

A total of 71 IPF patients were recruited at diagnosis. All patients underwent a standard chest CT-scan and a bioelectrical impedance analysis considered as reference standard for estimating malnutrition through the use of the fat-free mass index (FFMI). The skeletal muscle index (SMI) was measured on chest-CT at the level of the first lumbar vertebra by two radiologists. Lung fibrosis extent was quantified by three radiologists in consensus. The extent of emphysema, the pulmonary artery to aorta (PA/AO) diameter ratio and lymph node enlargement were also reported. Mortality and hospitalization over a 14-month follow-up were recorded.

RESULTS

A low FFMI defining malnutrition was identified in 26.8% of patients. SMI was significantly lower in these patients (p<0.001) and was correlated with FFMI (r=0.637, p<0.001). Interobserver agreement of SMI measurement was very good (ICC=0.91). For diagnosing malnutrition, SMI showed a 0.79 sensitivity, a 0.69 specificity, a 0.48 PPV and a 0.90 NPV. In univariate analysis, fibrosis extent was significantly associated with death, while SMI did not reach significance. In multivariate analysis, fibrosis extent and PA/AO ratio were independently associated with hospitalization.

CONCLUSIONS

SMI measured on chest CT could be a reliable tool to exclude malnutrition in IPF. A quantitative analysis of both fibrosis and skeletal muscle may allow holistic management of IPF patients.

Fibroblast Activation Protein-Specific PET/CT Imaging in Fibrotic Interstitial Lung Diseases and Lung Cancer: A Translational Exploratory Study

Interstitial lung diseases (ILDs) comprise over 200 parenchymal lung disorders. Among them, fibrosing ILDs, especially idiopathic pulmonary fibrosis, are associated with a poor prognosis, whereas some other ILDs, such as sarcoidosis, have a much better prognosis. A high proportion manifests as fibrotic ILD (fILD). Lung cancer (LC) is a frequent complication of fILD. Activated fibroblasts are crucial for fibrotic processes in fILD. The aim of this exploratory study was to evaluate the imaging properties of static and dynamic fibroblast activation protein (FAP) inhibitor (FAPI) PET/CT in various types of fILD and to confirm FAP expression in fILD lesions by FAP immunohistochemistry of human fILD biopsy samples and of lung sections of genetically engineered (Nedd4-2-/- ) mice with an idiopathic pulmonary fibrosislike lung disease. Methods: PET scans of 15 patients with fILD and suspected LC were acquired 10, 60, and 180 min after the administration of 150-250 MBq of a 68Ga-labeled FAPI tracer (FAPI-46). In 3 patients, dynamic scans over 40 min were performed instead of imaging after 10 min. The SUVmax and SUVmean of fibrotic lesions and LC were measured and CT-density-corrected. Target-to-background ratios (TBRs) were calculated. PET imaging was correlated with CT-based fibrosis scores. Time-activity curves derived from dynamic imaging were analyzed. FAP immunohistochemistry of 4 human fILD biopsy samples and of fibrotic lungs of Nedd4-2-/- mice was performed. Results: fILD lesions as well as LC showed markedly elevated 68Ga-FAPI uptake (density-corrected SUVmax and SUVmean 60 min after injection: 11.12 ± 6.71 and 4.29 ± 1.61, respectively, for fILD lesions and 16.69 ± 9.35 and 6.44 ± 3.29, respectively, for LC) and high TBR (TBR of density-corrected SUVmax and SUVmean 60 min after injection: 2.30 ± 1.47 and 1.67 ± 0.79, respectively, for fILD and 3.90 ± 2.36 and 2.37 ± 1.14, respectively, for LC). SUVmax and SUVmean decreased over time, with a stable TBR for fILD and a trend toward an increasing TBR in LC. Dynamic imaging showed differing time-activity curves for fILD and LC. 68Ga-FAPI uptake showed a positive correlation with the CT-based fibrosis index. Immunohistochemistry of human biopsy samples and the lungs of Nedd4-2-/- mice showed a patchy expression of FAP in fibrotic lesions, preferentially in the transition zone to healthy lung parenchyma. Conclusion:68Ga-FAPI PET/CT imaging is a promising new imaging modality for fILD and LC. Its potential clinical value for monitoring and therapy evaluation of fILD should be investigated in future studies.

A Computed Tomography Radiomics-Based Prediction Model on Interstitial Lung Disease in Anti-MDA5-Positive Dermatomyositis

Objectives: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5⁺ DM-ILD) is a life-threatening disease. The current study aimed to quantitatively assess the pulmonary high-resolution computed tomography (HRCT) images of MDA5⁺ DM-ILD by applying the radiomics approach and establish a multidimensional risk prediction model for the 6-month mortality.

Methods: This retrospective study was conducted in 228 patients from two centers, namely, a derivation cohort and a longitudinal internal validation cohort in Renji Hospital, as well as an external validation cohort in Guangzhou. The derivation cohort was randomly divided into training and testing sets. The primary outcome was 6-month all-cause mortality since the time of admission. Baseline pulmonary HRCT images were quantitatively analyzed by radiomics approach, and a radiomic score (Rad-score) was generated. Clinical predictors selected by univariable Cox regression were further incorporated with the Rad-score, to enhance the prediction performance of the final model (Rad-score plus model). In parallel, an idiopathic pulmonary fibrosis (IPF)-based visual CT score and ILD-GAP score were calculated as comparators.

Results: The Rad-score was significantly associated with the 6-month mortality, outperformed the traditional visual score and ILD-GAP score. The Rad-score plus model was successfully developed to predict the 6-month mortality, with C-index values of 0.88 [95% confidence interval (CI), 0.79–0.96] in the training set (n = 121), 0.88 (95%CI, 0.71–1.0) in the testing set (n = 31), 0.83 (95%CI, 0.68–0.98) in the internal validation cohort (n = 44), and 0.84 (95%CI, 0.64–1.0) in the external validation cohort (n = 32).

Conclusions: The radiomic feature was an independent and reliable prognostic predictor for MDA5⁺ DM-ILD.

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.

Childhood Interstitial Lung Disease: Imaging Guidelines and Recommendations

Childhood interstitial lung disease (ChILD) is an umbrella term encompassing a diverse group of diffuse lung diseases affecting infants and children. Although the timely and accurate diagnosis of ChILD is often challenging, it is optimally achieved through the multidisciplinary integration of imaging findings with clinical data, genetics, and potentially lung biopsy. This article reviews the definition and classification of ChILD; the role of imaging, pathology, and genetics in ChILD diagnosis; treatment options; and future goals. In addition, a practical approach to ChILD imaging based on the latest available research and the characteristic imaging appearance of ChILD entities are presented.

The role of imaging in the diagnosis and management of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease lacking a definite etiology, characterized by the nonspecific symptoms of dyspnea and dry cough. Due to its poor prognosis, imaging techniques play an essential role in diagnosing and managing IPF. High resolution computed tomography (HRCT) has been shown to be the most sensitive modality for the diagnosis of pulmonary fibrosis. It is the primary imaging modality used for the assessment and follow-up of patients with IPF. Other not commonly used imaging methods are under research, such as ultrasound, magnetic resonance imaging and positron emission tomography-computed tomography are alternative imaging techniques. This literature review aims to provide a brief overview of the imaging of IPF-related alterations.

Utility of a Deep Learning Algorithm for Detection of Reticular Opacity on Chest Radiograph in Patients with Interstitial Lung Disease

Background: Deep learning has been heavily explored for pulmonary nodule detection on chest radiograph. Reticular opacity detection in interstitial lung disease (ILD) is challenging and may also benefit from a deep learning algorithm (DLA). Objective: To evaluate the utility of DLA for detection of reticular opacity on chest radiographs in patients with surgically confirmed ILD. Methods: This retrospective study included 197 patients (130 men, 67 women; mean age 62.6±7.6 years) with surgically proven ILD between January 2017 and December 2018 who underwent preoperative chest radiograph and chest CT within a 30-day interval. A total of 197 age- and sex-matched control patients with normal chest radiographs were randomly selected. Commercially available DLA was used to detect lower lobe or subpleural abnormalities; those matching reticular opacity location on CT were deemed true-positives. Six readers (three thoracic radiologists; three residents) independently reviewed radiographs for reticular opacity presence with and without DLA. Interobserver agreement was assessed. Diagnostic performance was compared among interpretations. Subanalysis was performed according to CT-based classification of reticular opacity severity. DLA performance was also assessed in 102 chest radiographs from a different institution (51 with ILD; 51 matched controls). Results: Interobserver agreement was moderate (κ=0.517) for readers alone versus almost perfect (κ=0.870) for readers with DLA. Sensitivity, specificity, and accuracy for reticular opacity for DLA were 98.0%, 99.0%, and 98.5%; for pooled readers alone were 77.3%, 92.3%, and 84.8%; and for readers with DLA were 93.8%, 97.3%, and 95.6%. All metrics were significantly better (all p≤.002) for DLA and for readers with DLA compared with readers alone. Sensitivity for readers without and with DLA were 66.7% and 86.8% in mild disease, 84.2% and 98.8% in moderate disease, and 87.3% and 100.0% in severe disease. DLA exhibited 100.0% accuracy in the cases from the second center. Conclusions: DLA outperformed readers in reticular opacity detection, and use of DLA improved reader performance and interobservser agreement. Benefit of DLA was more notable in terms of sensitivity than specificity and was maintained in mild disease. Clinical Impact: Use of DLA may facilitate detection of reticular opacity on chest radiograph in the early stages of ILD.

Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis

Background

Radiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis (“radiomics”) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.

Methods

We investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.

Results

Radiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.

Conclusions

Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

CT-based radiomics decodes phenotypic, prognostic and molecular differences in SSc-ILD, and predicts progression-free survival with a significant impact on future clinical decision making in SSc-ILDhttps://bit.ly/3zPaMOn

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

BACKGROUND

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

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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