Practical Imaging Interpretation in Patients Suspected of Having Idiopathic Pulmonary Fibrosis: Official Recommendations from the Radiology Working Group of the Pulmonary Fibrosis Foundation

Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning

Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD.

Diagnostic Approach to Interstitial Lung Diseases Associated with Connective Tissue Diseases

Interstitial lung disorders are a group of respiratory diseases characterized by interstitial compartment infiltration, varying degrees of infiltration, and fibrosis, with or without small airway involvement. Although some are idiopathic (e.g., idiopathic pulmonary fibrosis, idiopathic interstitial pneumonias, and sarcoidosis), the great majority have an underlying etiology, such as systemic autoimmune rheumatic disease (SARD, also called Connective Tissue Diseases or CTD), inhalational exposure to organic matter, medications, and rarely, genetic disorders. This review focuses on diagnostic approaches in interstitial lung diseases associated with SARDs. To make an accurate diagnosis, a multidisciplinary, personalized approach is required, with input from various specialties, including pulmonary, rheumatology, radiology, and pathology, to reach a consensus. In a minority of patients, a definitive diagnosis cannot be established. Their clinical presentations and prognosis can be variable even within subsets of SARDs.

Central Role of CT in Management of Pulmonary Fibrosis

With the approval of antifibrotic medications to treat patients with idiopathic pulmonary fibrosis and progressive pulmonary fibrosis, radiologists have an integral role in diagnosing these entities and guiding treatment decisions. CT features of early pulmonary fibrosis include irregular thickening of interlobular septa, pleura, and intralobular linear structures, with subsequent progression to reticular abnormality, traction bronchiectasis or bronchiolectasis, and honeycombing. CT patterns of fibrotic lung disease can often be reliably classified on the basis of the CT features and distribution of the condition. Accurate identification of usual interstitial pneumonia (UIP) or probable UIP patterns by radiologists can obviate the need for a tissue sample-based diagnosis. Other entities that can appear as a UIP pattern must be excluded in multidisciplinary discussion before a diagnosis of idiopathic pulmonary fibrosis is made. Although the imaging findings of nonspecific interstitial pneumonia and fibrotic hypersensitivity pneumonitis can overlap with those of a radiologic UIP pattern, these entities can often be distinguished by paying careful attention to the radiologic signs. Diagnostic challenges may include misdiagnosis of fibrotic lung disease due to pitfalls such as airspace enlargement with fibrosis, paraseptal emphysema, recurrent aspiration, and postinfectious fibrosis. The radiologist also plays an important role in identifying complications of pulmonary fibrosis-pulmonary hypertension, acute exacerbation, infection, and lung cancer in particular. In cases in which there is uncertainty regarding the clinical and radiologic diagnoses, surgical biopsy is recommended, and a multidisciplinary discussion among clinicians, radiologists, and pathologists can be used to address diagnosis and management strategies. This review is intended to help radiologists diagnose and manage pulmonary fibrosis more accurately, ultimately aiding in the clinical management of affected patients. ©RSNA, 2024 Supplemental material is available for this article.

Complications of fibrotic interstitial lung disease for the general radiologist

Interstitial lung diseases (ILDs) are a heterogeneous group of conditions characterised by non-infective inflammation and scarring of the lung parenchyma. They are not infrequently encountered by the general radiologist in both acute and outpatient reporting settings who may even be the first to make the diagnosis. In the acute setting, patients with ILD can present with respiratory deterioration due to a number of causes and in addition to the common causes of dyspnoea, an acute exacerbation of ILD needs to be considered. An exacerbation can be initiated by common triggers such as infection, pulmonary embolism (PE), and heart failure, and it can also be initiated by an insult to the lung or occur due to an unknown cause. Particular care needs to be taken when interpreting computed tomography (CT) examinations in these patients as the findings of an acute exacerbation are non-specific and patient and technical factors can cause spurious appearances including dependent changes, breathing artefact and contrast medium opacification. In the non-acute setting, patients with ILD are at increased risk of lung cancer and pulmonary hypertension (PH), with lung cancer being a particularly important consideration as treatments carry the risk of triggering an acute exacerbation or deterioration in lung function. Overall, this review aims to provide an overview for the general radiologist of additional factors to consider when interpreting scans in patients with ILD and how the presence of ILD impacts the differential diagnoses and complications that can occur in these patients in both acute and non-acute settings.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

High-Resolution CT Scan Fibrotic Patterns in Stage IV Pulmonary Sarcoidosis: Impact on Pulmonary Function and Survival

BACKGROUND

Different patterns of fibrosis on high-resolution CT scans (HRCT) have been associated with reduced survival in some interstitial lung diseases. Nothing is known about HRCT scan patterns and survival in sarcoidosis.

RESEARCH QUESTION

Will a detailed description of the extent and pattern of HRCT scan fibrosis in patients with stage IV pulmonary sarcoidosis impact pulmonary function and survival?

STUDY DESIGN AND METHODS

Two hundred forty patients with stage IV sarcoidosis at two large tertiary institutions were studied. The earliest HRCT scan with fibrosis was reviewed for extent of fibrosis (< 10%, 10%-20%, and > 20%) and presence of bronchiectasis, upper lobe fibrocystic changes, basal subpleural honeycombing, ground-glass opacities (GGOs), large bullae, and mycetomas. Presence of sarcoidosis-associated pulmonary hypertension (SAPH) and pulmonary function testing performed within 1 year of HRCT were recorded. Patients were followed up until last clinic visit, death, or lung transplantation.

RESULTS

The mean age was 58.4 years. Seventy-four percent were Black, 63% were female, and mean follow-up was 7.4 years. Death or LT occurred in 53 patients (22%). Thirty-one percent had > 20% fibrosis, 25% had 10%-20% fibrosis, and 44% had < 10% fibrosis. The most common HRCT abnormalities were bronchiectasis (76%), upper lobe fibrocystic changes (36%), and GGOs (28%). Twelve percent had basal subpleural honeycombing, and 32% had SAPH. Patients with > 20% fibrosis had more severe pulmonary impairment, were more likely to have SAPH (53%), and had worse survival (44% mortality; P < .001). Upper lobe fibrocystic changes, basal subpleural honeycombing, and large bullae were associated with worse pulmonary function and worse survival. Patients with basal subpleural honeycombing had the worst pulmonary function and survival (55% mortality; P < .001). GGOs were associated with worse pulmonary function but not worse survival, and mycetomas were associated with worse survival but not worse pulmonary function. A Cox proportional hazards model indicated that basal subpleural honeycombing (hazard ratio, 7.95), diffusion capacity for carbon monoxide < 40% (HR, 5.67) and White race (hazard ratio, 2.61) were independent predictors of reduced survival.

INTERPRETATION

HRCT scan features of fibrotic pulmonary sarcoidosis had an impact on pulmonary function and survival. Presence of >20% fibrosis and basal subpleural honeycombing are predictive of worse pulmonary function and worse survival in patients with stage IV pulmonary sarcoidosis.

Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases

Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively.

Thin-Section CT in the Categorization and Management of Pulmonary Fibrosis including Recently Defined Progressive Pulmonary Fibrosis

While idiopathic pulmonary fibrosis (IPF) is the most common type of fibrotic lung disease, there are numerous other causes of pulmonary fibrosis that are often characterized by lung injury and inflammation. Although often gradually progressive and responsive to immune modulation, some cases may progress rapidly with reduced survival rates (similar to IPF) and with imaging features that overlap with IPF, including usual interstitial pneumonia (UIP)-pattern disease characterized by peripheral and basilar predominant reticulation, honeycombing, and traction bronchiectasis or bronchiolectasis. Recently, the term progressive pulmonary fibrosis has been used to describe non-IPF lung disease that over the course of a year demonstrates clinical, physiologic, and/or radiologic progression and may be treated with antifibrotic therapy. As such, appropriate categorization of the patient with fibrosis has implications for therapy and prognosis and may be facilitated by considering the following categories: (a) radiologic UIP pattern and IPF diagnosis, (b) radiologic UIP pattern and non-IPF diagnosis, and (c) radiologic non-UIP pattern and non-IPF diagnosis. By noting increasing fibrosis, the radiologist contributes to the selection of patients in which therapy with antifibrotics can improve survival. As the radiologist may be first to identify developing fibrosis and overall progression, this article reviews imaging features of pulmonary fibrosis and their significance in non-IPF-pattern fibrosis, progressive pulmonary fibrosis, and implications for therapy. Keywords: Idiopathic Pulmonary Fibrosis, Progressive Pulmonary Fibrosis, Thin-Section CT, Usual Interstitial Pneumonia © RSNA, 2024.

Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT

Background There is clinical need to better quantify lung disease severity in pulmonary hypertension (PH), particularly in idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-LD). Purpose To quantify fibrosis on CT pulmonary angiograms using an artificial intelligence (AI) model and to assess whether this approach can be used in combination with radiologic scoring to predict survival. Materials and Methods This retrospective multicenter study included adult patients with IPAH or PH-LD who underwent incidental CT imaging between February 2007 and January 2019. Patients were divided into training and test cohorts based on the institution of imaging. The test cohort included imaging examinations performed in 37 external hospitals. Fibrosis was quantified using an established AI model and radiologically scored by radiologists. Multivariable Cox regression adjusted for age, sex, World Health Organization functional class, pulmonary vascular resistance, and diffusing capacity of the lungs for carbon monoxide was performed. The performance of predictive models with or without AI-quantified fibrosis was assessed using the concordance index (C index). Results The training and test cohorts included 275 (median age, 68 years [IQR, 60-75 years]; 128 women) and 246 (median age, 65 years [IQR, 51-72 years]; 142 women) patients, respectively. Multivariable analysis showed that AI-quantified percentage of fibrosis was associated with an increased risk of patient mortality in the training cohort (hazard ratio, 1.01 [95% CI: 1.00, 1.02]; P = .04). This finding was validated in the external test cohort (C index, 0.76). The model combining AI-quantified fibrosis and radiologic scoring showed improved performance for predicting patient mortality compared with a model including radiologic scoring alone (C index, 0.67 vs 0.61; P < .001). Conclusion Percentage of lung fibrosis quantified on CT pulmonary angiograms by an AI model was associated with increased risk of mortality and showed improved performance for predicting patient survival when used in combination with radiologic severity scoring compared with radiologic scoring alone. © RSNA, 2024 Supplemental material is available for this article.

Deep Learning Assessment of Progression of Emphysema and Fibrotic Interstitial Lung Abnormality

Rationale: Although studies have evaluated emphysema and fibrotic interstitial lung abnormality individually, less is known about their combined progression. Objectives: To define clinically meaningful progression of fibrotic interstitial lung abnormality in smokers without interstitial lung disease and evaluate the effects of fibrosis and emphysema progression on mortality. Methods: Emphysema and pulmonary fibrosis were assessed on the basis of baseline and 5-year follow-up computed tomography scans of 4,450 smokers in the COPDGene Study using deep learning algorithms. Emphysema was classified as absent, trace, mild, moderate, confluent, or advanced destructive. Fibrosis was expressed as a percentage of lung volume. Emphysema progression was defined as an increase by at least one grade. A hybrid distribution and anchor-based method was used to determine the minimal clinically important difference in fibrosis. The relationship between progression and mortality was evaluated using multivariable shared frailty models using an age timescale. Measurements and Main Results: The minimal clinically important difference for fibrosis was 0.58%. On the basis of this threshold, 2,822 (63%) had progression of neither emphysema nor fibrosis, 841 (19%) had emphysema progression alone, 512 (12%) had fibrosis progression alone, and 275 (6.2%) had progression of both. Compared with nonprogressors, hazard ratios for mortality were 1.42 (95% confidence interval, 1.11-1.82) in emphysema progressors, 1.49 (1.14-1.94) in fibrosis progressors, and 2.18 (1.58-3.02) in those with progression of both emphysema and fibrosis. Conclusions: In smokers without known interstitial lung disease, small changes in fibrosis may be clinically significant, and combined progression of emphysema and fibrosis is associated with increased mortality.

Delayed inflammatory pulmonary syndrome: A distinct clinical entity in the spectrum of inflammatory syndromes in COVID-19 infection?

BACKGROUND

During the second wave of the coronavirus disease 2019 (COVID-19) pandemic, a subset of critically ill patients developed delayed respiratory deterioration in the absence of new infection, fluid overload or extra-pulmonary organ dysfunction.

AIM

To describe the clinical and laboratory characteristics, outcomes, and management of these patients, and to contrast this entity with other post COVID-19 immune dysregulation related inflammatory disorders.

METHODS

This was a retrospective observational study of adult patients admitted to the medical intensive care unit of a 2200-bed university affiliated teaching hospital, between May and August 2021, who fulfilled clearly defined inclusion and exclusion criteria. Outcome was assessed by a change in PaO2/FiO2 ratio and levels of inflammatory markers before and after immunomodulation, duration of mechanical ventilation after starting treatment, and survival to discharge.

RESULTS

Five patients developed delayed respiratory deterioration in the absence of new infection, fluid overload or extra-pulmonary organ dysfunction at a median interquartile range (IQR) duration of 32 (23-35) d after the onset of symptoms. These patients had elevated inflammatory markers, required mechanical ventilation for 13 (IQR 10-23) d, and responded to glucocorticoids and/or intravenous immunoglobulin. One patient died (20%).

CONCLUSION

This delayed respiratory worsening with elevated inflammatory markers and clinical response to immunomodulation appears to contrast the well described Multisystem Inflammatory Syndrome - Adults by the paucity of extrapulmonary organ involvement. The diagnosis can be considered in patients presenting with delayed respiratory worsening, that is not attributable to cardiac dysfunction, fluid overload or ongoing infections, and associated with an increase in systemic inflammatory markers like C-reactive protein, inteleukin-6 and ferritin. A good response to immunomodulation can be expected. This delayed inflammatory pulmonary syndrome may represent a distinct clinical entity in the spectrum of inflammatory syndromes in COVID-19 infection.

Arsenic exposure associated with lung interstitial changes in non-smoking individuals living near a petrochemical complex: A repeated cross-sectional study

Arsenic exposure is associated with airway inflammation and decreased lung function tests. Whether arsenic exposure associated with lung interstitial changes remains unknown. We conducted this population-based study in southern Taiwan during 2016 and 2018. Our study recruited individuals aged over 20 years, residing in the vicinity of a petrochemical complex and with no history of cigarette smoking. In both the 2016 and 2018 cross-sectional studies, we conducted chest low-dose computed tomography (LDCT) scans, as well as urinary arsenic and blood biochemistry analyses. Lung interstitial changes included lung fibrotic changes that were defined as the presence of curvilinear or linear densities, fine lines, or plate opacity in specific lobes; additionally, other interstitial changes were defined as the presence of ground-glass opacity (GGO) or bronchiectasis on the LDCT images. In both cross-sectional studies conducted in 2016 and 2018, participants with lung fibrotic changes exhibited a statistically significant increase in the mean urinary arsenic concentrations compared to those without fibrotic changes (geometric mean = 100.1 vs. 82.8 μg/g creatinine, p < 0.001 for cross-sectional study 2016, and geometric mean = 105.6 vs. 71.0 μg/g creatinine, p < 0.001 for cross-sectional study 2018). After controlling for age, gender, body mass index, platelet counts, hypertension, aspartate aminotransferase, cholesterol, HbA1c, and educational levels, we observed a significant positive association between a unit increase in log urinary arsenic concentrations and the risk of lung fibrotic changes in both cross-sectional study 2016 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.04-1.90, p = 0.028) and cross-sectional study 2018 (OR = 3.03, 95% CI = 1.38-6.63, p = 0.006). Our study did not find a significant association between arsenic exposure and bronchiectasis or GGO. It is imperative for the government to take significant measures to reduce arsenic exposure levels among individuals living near petrochemical complexes.

High-Resolution Computed Tomography of Fibrotic Interstitial Lung Disease

While radiography is the first-line imaging technique for evaluation of pulmonary disease, high-resolution computed tomography (HRCT) provides detailed assessment of the lung parenchyma and interstitium, allowing normal anatomy to be differentiated from superimposed abnormal findings. The fibrotic interstitial lung diseases have HRCT features that include reticulation, traction bronchiectasis and bronchiolectasis, honeycombing, architectural distortion, and volume loss. The characterization and distribution of these features result in distinctive CT patterns. The CT pattern and its progression over time can be combined with clinical, serologic, and pathologic data during multidisciplinary discussion to establish a clinical diagnosis. Serial examinations identify progression, treatment response, complications, and can assist in determining prognosis. This article will describe the technique used to perform HRCT, the normal and abnormal appearance of the lung on HRCT, and the CT patterns identified in common fibrotic lung diseases.

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.

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Background and Objectives: Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. Materials and Methods: 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. Results: The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters' variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan. Conclusions: A CN based computer aided diagnosis could provide the much-required data needed to successfully manage DILDs.

Interstitial Lung Fibrosis Following COVID-19 Pneumonia

Background and Objectives: Pulmonary fibrosis represents a stage of normal physiologic response to inflammatory aggression, mostly self-limiting and reversible; however, numerous patients treated for SARS-CoV-2 pneumonia present after release from hospital residual lung fibrosis. In this article, we aim to present an optimization method for evaluating pulmonary fibrosis by quantitative analysis, to identify the risk factors/predictors for pulmonary fibrosis in patients with SARS-CoV-2 infection, and to characterize the impact of pulmonary fibrosis on the symptomatology of patients after release from the hospital. Materials and Methods: We performed a prospective observational study on 100 patients with severe forms of pneumonia, with a control group of 61 non-COVID normal patients. Results: We found persistent interstitial changes consistent with fibrotic changes in 69% of patients. The risk of fibrosis was proportional to the values of erythrocyte sedimentation rate (ESR), C reactive protein (CRP), and lactate dehydrogenase (LDH), and to the duration of hospitalization. The imaging parameters correlated with increased risk for interstitial fibrosis were the number of affected pulmonary lobes and the percent of interstitial pulmonary fibrosis. Conclusions: The main risk factors for pulmonary fibrosis post-COVID-19 identified in our study are increased ESR, CRP, LDH, duration of hospitalization and the severity of pneumonia.

A Novel Method for Lung Image Processing Using Complex Networks

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in three dimensions: emphysema, ground glass opacity, and consolidation. This method was evaluated on a 60-patient lot and the results showed a clear, quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

Quantitative analysis of high-resolution computed tomography features of idiopathic pulmonary fibrosis: a structure-function correlation study

BACKGROUND

The quantitative analysis of high-resolution computed tomography (HRCT) is increasingly being used to quantify the severity and evaluate the prognosis of disease. Our aim was to quantify the HRCT features of idiopathic pulmonary fibrosis (IPF) and identify their association with pulmonary function tests.

METHODS

This was a retrospective, single-center, clinical research study. Patients with IPF were retrospectively included. Pulmonary segmentation was performed using the deep learning-based method. Radiologists manually segmented 4 findings of IPF, including honeycombing (HC), reticular pattern (RE), traction bronchiectasis (TRBR), and ground glass opacity (GGO). Pulmonary vessels were segmented with the automatic integration segmentation method. All segmentation results were quantified by the corresponding segmentation software. Correlations between the volume of the 4 findings on HRCT, volume of the lesions at different sites, pulmonary vascular-related parameters, and pulmonary function tests were analyzed.

RESULTS

A total of 101 IPF patients (93 males) with a median age of 63 years [interquartile range (IQR), 58 to 68 years] were included in this study. Total lesion extent demonstrated a stronger negative correlation with diffusion capacity for carbon monoxide (DLco) compared to HC, RE, and TRBR [total lesion ratio, correlation coefficient (r) =-0.67, P<0.001; HC, r=-0.45, P<0.001; RE, r=-0.41, P<0.001; TRBR, r=-0.25, P<0.05, respectively]. Correlations with lung function were similar among various lesion sites with r from -0.38 to -0.61 (P<0.001). Pulmonary artery volume (PAV) displayed a slightly increased positive association with the DLco compared to total pulmonary vascular volume (PVV); for PAV, r=0.41 and P<0.001 and for total PVV, r=0.36 and P<0.001. Additionally, total lesion extent, HC, and RE indicated a negative relationship with vascular-related parameters, and the strength of the correlations was independent of lesion site.

CONCLUSIONS

Quantitative analysis of HRCT features of IPF indicated a decline in function and an aggravation of vascular destruction with increasing lesion extent. Furthermore, a positive correlation between vascular-related parameters and pulmonary function was confirmed. This co-linearity indicated the potential of vascular-related parameters as new objective markers for evaluating the severity of IPF.

Relationship between Mechanical Ventilation and Histological Fibrosis in Patients with Acute Respiratory Distress Syndrome Undergoing Open Lung Biopsy

Background: Mechanical ventilation brings the risk of ventilator-induced lung injury, which can lead to pulmonary fibrosis and prolonged mechanical ventilation. Methods: A retrospective analysis of patients with acute respiratory distress syndrome (ARDS) who received open lung biopsy between March 2006 and December 2019. Results: A total of 68 ARDS patients receiving open lung biopsy with diffuse alveolar damage (DAD; the hallmark pathology of ARDS) were analyzed and stratified into non-fibrosis (n = 56) and fibrosis groups (n = 12). The duration of ventilator usage and time spent in the intensive care unit and hospital stay were all significantly higher in the fibrosis group. Hospital mortality was higher in the fibrosis than in the non-fibrosis group (67% vs. 57%, p = 0.748). A multivariable logistic regression model demonstrated that mechanical power at ARDS diagnosis and ARDS duration before biopsy were independently associated with histological fibrosis at open lung biopsy (odds ratio 1.493 (95% CI 1.014–2.200), p = 0.042; odds ratio 1.160 (95% CI 1.052–1.278), p = 0.003, respectively). Conclusions: Our findings indicate that prompt action aimed at staving off injurious mechanical stretching of lung parenchyma and subsequent progression to fibrosis may have a positive effect on clinical outcomes.

Idiopathic pulmonary fibrosis: Current and future treatment

Objectives

Idiopathic pulmonary fibrosis (IPF) is a chronic fibrotic lung disease characterized by dry cough, fatigue, and progressive exertional dyspnea. Lung parenchyma and architecture is destroyed, compliance is lost, and gas exchange is compromised in this debilitating condition that leads inexorably to respiratory failure and death within 3–5 years of diagnosis. This review discusses treatment approaches to IPF in current use and those that appear promising for future development.

Data Source

The data were obtained from the Randomized Controlled Trials and scientific studies published in English literature. We used search terms related to IPF, antifibrotic treatment, lung transplant, and management.

Results

Etiopathogenesis of IPF is not fully understood, and treatment options are limited. Pathological features of IPF include extracellular matrix remodeling, fibroblast activation and proliferation, immune dysregulation, cell senescence, and presence of aberrant basaloid cells. The mainstay therapies are the oral antifibrotic drugs pirfenidone and nintedanib, which can improve quality of life, attenuate symptoms, and slow disease progression. Unilateral or bilateral lung transplantation is the only treatment for IPF shown to increase life expectancy.

Conclusion

Clearly, there is an unmet need for accelerated research into IPF mechanisms so that progress can be made in therapeutics toward the goals of increasing life expectancy, alleviating symptoms, and improving well‐being.

The Role of Surgical Lung Biopsy in the Diagnosis of Fibrotic Interstitial Lung Disease: Perspective from the Pulmonary Fibrosis Foundation

Diagnosis of interstitial lung disease (ILD) requires a multidisciplinary diagnosis (MDD) approach that includes clinicians, radiologists, and pathologists. Surgical lung biopsy (SLB) is currently the recommended standard in obtaining pathological specimens for patients with ILD requiring a tissue diagnosis. The increased diagnostic confidence and accuracy provided by microscopic pathology assessment of SLB specimens must be balanced with the associated risks in ILD patients. This document was developed by the Surgical Lung Biopsy Working Group of the Pulmonary Fibrosis Foundation, composed of a multidisciplinary group of ILD physicians including pulmonologists, radiologists, pathologists, and thoracic surgeons. In this document, we present an up-to-date literature review of the indications, contraindications, risks, and alternatives to SLB in the diagnosis of fibrotic ILD, outline an integrated approach to the decision-making around SLB in the diagnosis of fibrotic ILD, and provide practical information to maximize the yield and safety of SLB.

Evaluation of Correlations between Genetic Variants and High-Resolution Computed Tomography Patterns in Idiopathic Pulmonary Fibrosis

Background. Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease (ILD). This prospective observational study aimed at the evaluation of any correlation between genetic variants associated with IPF susceptibility and high-resolution computed tomography (HRCT) patterns. It also aimed at evidencing any differences in the HRTC pattern between the familial and sporadic form at diagnosis and after two years. Methods. A total of 65 IPF patients (mean age at diagnosis 65 ± 10) were enrolled after having given written informed consent. HRCT and genetic evaluations were performed. Results. A total of 19 familial (mean age 62 ± 15) and 46 sporadic (mean age 70 ± 9) IPF patients were enrolled. A statistically significant difference was evidenced in the HRTC pattern at diagnosis between the two groups. Sporadic IPF patients had a predominantly usual interstitial pneumonia (UIP) pattern compared with those patients with familial IPF (60.0% vs. 21.1%, respectively). Moreover, familial IPF patients had more alternative diagnoses than those with sporadic IPF (31.6% vs. 2.2%, respectively). Furthermore, there was a slight increase in the typical UIP pattern in the familial IPF group at two years from diagnosis. Conclusions. Genetic factors play a pivotal role in the risk of developing IPF. However, further studies are required to clarify how these genetic factors may guide clinical treatment decisions.