[Progressive pulmonary Fibrosis]. / Progressive Pulmonale Fibrose PPF.

INTRODUCTION: Progressive pulmonary Fibrosis Abstract: Cough and dyspnea on excertion are common and early symptoms of interstitial lung diseases (ILD). Thoracic imaging (particularly computed tomography) detects such lung structural alterations early in the disease course. Knowledge of these diseases and their management is necessary in the daily business. The term "progressive pulmonary fibrosis" subsumes a heterogene group of interstitial lung diseases with a similar course of progressive fibrosis. The management of these diseases should be discussed interdisciplinary, similar to the management of the Idiopathic pulmonary fibrosis (IPF). Antifibrotic drugs are new therapeutic options.

[64Cu]Cu-PEG-FUD peptide for noninvasive and sensitive detection of murine pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease resulting in irreversible scarring within the lungs. However, the lack of biomarkers that enable real-time assessment of disease activity remains a challenge in providing efficient clinical decision-making and optimal patient care in IPF. Fibronectin (FN) is highly expressed in fibroblastic foci of the IPF lung where active extracellular matrix (ECM) deposition occurs. Functional upstream domain (FUD) tightly binds the N-terminal 70-kilodalton domain of FN that is crucial for FN assembly. In this study, we first demonstrate the capacity of PEGylated FUD (PEG-FUD) to target FN deposition in human IPF tissue ex vivo. We subsequently radiolabeled PEG-FUD with 64Cu and monitored its spatiotemporal biodistribution via µPET/CT imaging in mice using the bleomycin-induced model of pulmonary injury and fibrosis. We demonstrated [64Cu]Cu-PEG-FUD uptake 3 and 11 days following bleomycin treatment, suggesting that radiolabeled PEG-FUD holds promise as an imaging probe in aiding the assessment of fibrotic lung disease activity.

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT.

PURPOSE: Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images. MATERIALS AND METHODS: A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient. RESULTS: The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm's categorisation was a significant predictor of IPF progression. CONCLUSIONS: The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

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.

Association between body fat decrease during the first year after diagnosis and the prognosis of idiopathic pulmonary fibrosis: CT-based body composition analysis.

BACKGROUND: The prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF. METHODS: This single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020. The fat area (cm2, sum of subcutaneous and visceral fat) and muscle area (cm2) at the T12-L1 level were obtained from chest CT images using a fully automatic deep learning-based software. Changes in the body composition were dichotomized using thresholds dividing the lowest quartile and others, respectively (fat area: -52.3 cm2, muscle area: -7.4 cm2). Multivariable Cox regression analyses adjusted for PFT result and IPF extent on CT images and the log-rank test were performed to assess the association between the fat area change during the first year post-diagnosis and the composite outcome of death or lung transplantation. RESULTS: In total, 307 IPF patients (69.3 ± 8.1 years; 238 men) were included. During the first year post-diagnosis, fat area, muscle area, and body mass index (BMI) changed by -15.4 cm2, -1 cm2, and - 0.4 kg/m2, respectively. During a median follow-up of 47 months, 146 patients had the composite outcome (47.6%). In Cox regression analyses, a change in the fat area < -52.3 cm2 was associated with composite outcome incidence in models adjusted with baseline clinical variables (hazard ratio [HR], 1.566, P = .022; HR, 1.503, P = .036 in a model including gender, age, and physiology [GAP] index). This prognostic value was consistent when adjusted with one-year changes in clinical variables (HR, 1.495; P = .030). However, the change in BMI during the first year was not a significant prognostic factor (P = .941). Patients with a change in fat area exceeding this threshold experienced the composite outcome more frequently than their counterparts (58.4% vs. 43.9%; P = .007). CONCLUSION: A ≥ 52.3 cm2 decrease in fat area, automatically measured using deep learning technique, at T12-L1 in one year post-diagnosis was an independent poor prognostic factor in IPF patients.

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.

A formula for predicting emphysema extent in combined idiopathic pulmonary fibrosis and emphysema.

BACKGROUND: No single pulmonary function test captures the functional effect of emphysema in idiopathic pulmonary fibrosis (IPF). Without experienced radiologists, other methods are needed to determine emphysema extent. Here, we report the development and validation of a formula to predict emphysema extent in patients with IPF and emphysema. METHODS: The development cohort included 76 patients with combined IPF and emphysema at the Royal Brompton Hospital, London, United Kingdom. The formula was derived using stepwise regression to generate the weighted combination of pulmonary function data that fitted best with emphysema extent on high-resolution computed tomography. Test cohorts included patients from two clinical trials (n = 455 [n = 174 with emphysema]; NCT00047645, NCT00075998) and a real-world cohort from the Royal Brompton Hospital (n = 191 [n = 110 with emphysema]). The formula is only applicable for patients with IPF and concomitant emphysema and accordingly was not used to detect the presence or absence of emphysema. RESULTS: The formula was: predicted emphysema extent = 12.67 + (0.92 x percent predicted forced vital capacity) - (0.65 x percent predicted forced expiratory volume in 1 second) - (0.52 x percent predicted carbon monoxide diffusing capacity). A significant relationship between the formula and observed emphysema extent was found in both cohorts (R2 = 0.25, P < 0.0001; R2 = 0.47, P < 0.0001, respectively). In both, the formula better predicted observed emphysema extent versus individual pulmonary function tests. A 15% emphysema extent threshold, calculated using the formula, identified a significant difference in absolute changes from baseline in forced vital capacity at Week 48 in patients with baseline-predicted emphysema extent < 15% versus ≥ 15% (P = 0.0105). CONCLUSION: The formula, designed for use in patients with IPF and emphysema, demonstrated enhanced ability to predict emphysema extent versus individual pulmonary function tests. TRIAL REGISTRATION: NCT00047645; NCT00075998.

Imaging of Pulmonary Fibrosis: An Update, From the AJR Special Series on Imaging of Fibrosis.

Pulmonary fibrosis is recognized as occurring in association with a wide and increasing array of conditions, and it presents with a spectrum of chest CT appearances. Idiopathic pulmonary fibrosis (IPF), which corresponds histologically with usual interstitial pneumonia and represents the most common idiopathic interstitial pneumonia, is a chronic progressive fibrotic interstitial lung disease (ILD) of unknown cause. Progressive pulmonary fibrosis (PPF) describes the radiologic development of pulmonary fibrosis in patients with ILD of a known or unknown cause other than IPF. The recognition of PPF impacts management of patients with ILD-for example, in guiding initiation of antifibrotic therapy. Interstitial lung abnormalities are an incidental CT finding in patients without suspected ILD and may represent an early intervenable form of pulmonary fibrosis. Traction bronchiectasis and/or bronchiolectasis, when detected in the setting of chronic fibrosis, is generally considered evidence of irreversible disease, and progression predicts worsening mortality risk. Awareness of the association between pulmonary fibrosis and connective tissue diseases, particularly rheumatoid arthritis, is increasing. This review provides an update on the imaging of pulmonary fibrosis, with attention given to recent advances in disease understanding with relevance to radiologic practice. The essential role of a multidisciplinary approach to clinical and radiologic data is highlighted.

Evaluating Tissue Heterogeneity in the Radiologically Normal-Appearing Tissue in IPF Compared to Healthy Controls.

RATIONALE AND OBJECTIVES: Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease characterised by heterogeneously distributed fibrotic lesions. The inter- and intra-patient heterogeneity of the disease has meant that useful biomarkers of severity and progression have been elusive. Previous quantitative computed tomography (CT) based studies have focussed on characterising the pathological tissue. However, we hypothesised that the remaining lung tissue, which appears radiologically normal, may show important differences from controls in tissue characteristics. MATERIALS AND METHODS: Quantitative metrics were derived from CT scans in IPF patients (N = 20) and healthy controls with a similar age (N = 59). An automated quantitative software (CALIPER, Computer-Aided Lung Informatics for Pathology Evaluation and Rating) was used to classify tissue as normal-appearing, fibrosis, or low attenuation area. Densitometry metrics were calculated for all lung tissue and for only the normal-appearing tissue. Heterogeneity of lung tissue density was quantified as coefficient of variation and by quadtree. Associations between measured lung function and quantitative metrics were assessed and compared between the two cohorts. RESULTS: All metrics were significantly different between controls and IPF (p < 0.05), including when only the normal tissue was evaluated (p < 0.04). Density in the normal tissue was 14% higher in the IPF participants than controls (p < 0.001). The normal-appearing tissue in IPF had heterogeneity metrics that exhibited significant positive relationships with the percent predicted diffusion capacity for carbon monoxide. CONCLUSION: We provide quantitative assessment of IPF lung tissue characteristics compared to a healthy control group of similar age. Tissue that appears visually normal in IPF exhibits subtle but quantifiable differences that are associated with lung function and gas exchange.

Pictorial Review of Fibrotic Interstitial Lung Disease on High-Resolution CT Scan and Updated Classification.

TOPIC IMPORTANCE: Given the recently expanded approval of antifibrotics for various fibrotic interstitial lung diseases (ILDs), early and correct recognition of these diseases is imperative for physicians. Because high-resolution chest CT scan forms the backbone of diagnosis for ILD, this review will discuss evidence-based imaging findings of key fibrotic ILDs and an approach for differentiating these diseases. REVIEW FINDINGS: (1) Imaging findings of nonspecific interstitial pneumonia may evolve over time and become indistinguishable from usual interstitial pneumonia. Therefore, if remote imaging can be reviewed, this would increase the likelihood of an accurate imaging diagnosis, particularly if findings appear to represent a usual interstitial pneumonia pattern on the recent examination. (2) Given the difficulty and lack of objectivity in classifying patients with hypersensitivity pneumonitis into acute, subacute, and chronic categories and that prognosis depends primarily on presence or absence of fibrosis, the new set of guidelines released in 2020 categorizes patients with hypersensitivity pneumonitis as either nonfibrotic (purely inflammatory) or fibrotic (either purely fibrotic or mixed fibrotic/inflammatory) based on imaging and/or histologic findings, and the prior temporal terms are no longer used. (3) Interstitial lung abnormalities are incidental CT scan findings that may suggest early ILD in patients without clinical suspicion for ILD. Patients with high-risk features should undergo clinical evaluation for ILD and be actively monitored for disease progression. SUMMARY: Fibrotic ILD on high-resolution chest CT scan is a complex topic, but with use of an evidence-based analysis and algorithm as provided in this article, the probability of a correct imaging diagnosis increases.

Initial results with [18F]FAPI-74 PET/CT in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing interstitial lung disease with a poor prognosis. 68Ga-labeled FAP ligands exhibited highly promising results due to the crucial role of activated fibroblasts in fibrosis imaging of the lung. However, 18F-labeled FAP ligands might provide qualitatively much higher imaging results with accompanying economic benefits due to large-scale production. Thus, we sought to investigate the potential of [18F]FAPI-74 prospectively in a small patient cohort. METHODS: Eight patients underwent both [18F]FAPI-74-PET/CT and HRCT scans and were then compared with a control group without any fibrosing pulmonary disease. The tracer uptake of fibrotic lung areas was analyzed in synopsis with radiological and clinical parameters. RESULTS: We observed a positive correlation between the fibrotic active volume, the Hounsfield scale, as well as the vital and diffusing capacity of the lung. CONCLUSION: The initial results confirm our assumption that [18F]FAPI-74 offers a viable non-invasive assessment method for pulmonary fibrotic changes in patients with IPF.

External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis.

BACKGROUND: Previous work has shown the ability of Fibresolve, a machine learning system, to non-invasively classify idiopathic pulmonary fibrosis (IPF) with a pre-invasive sensitivity of 53% and specificity of 86% versus other types of interstitial lung disease. Further external validation for the use of Fibresolve to classify IPF in patients with non-definite usual interstitial pneumonia (UIP) is needed. The aim of this study is to assess the sensitivity for Fibresolve to positively classify IPF in an external cohort of patients with a non-definite UIP radiographic pattern. METHODS: This is a retrospective analysis of patients (n = 193) enrolled in two prospective phase two clinical trials that enrolled patients with IPF. We retrospectively identified patients with non-definite UIP on HRCT (n = 51), 47 of whom required surgical lung biopsy for diagnosis. Fibresolve was used to analyze the HRCT chest imaging which was obtained prior to invasive biopsy and sensitivity for final diagnosis of IPF was calculated. RESULTS: The sensitivity of Fibresolve for the non-invasive classification of IPF in patients with a non-definite UIP radiographic pattern by HRCT was 76.5% (95% CI 66.5-83.7). For the subgroup of 47 patients who required surgical biopsy to aid in final diagnosis of IPF, Fibresolve had a sensitivity of 74.5% (95% CI 60.5-84.7). CONCLUSION: In patients with suspected IPF with non-definite UIP on HRCT, Fibresolve can positively identify cases of IPF with high sensitivity. These results suggest that in combination with standard clinical assessment, Fibresolve has the potential to serve as an adjunct in the non-invasive diagnosis of IPF.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information. METHODS: A multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the "BRP Prognosis Model". The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed. RESULTS: Neutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2-3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration. CONCLUSIONS: In this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.

Detection of Pulmonary Fibrosis with a Collagen-Mimetic Peptide.

Idiopathic pulmonary fibrosis (IPF) is a disease of unknown etiology that is characterized by excessive deposition and abnormal remodeling of collagen. IPF has a mean survival time of only 2-5 years from diagnosis, creating a need to detect IPF at an earlier stage when treatments might be more effective. We sought to develop a minimally invasive probe that could detect molecular changes in IPF-associated collagen. Here, we describe the design, synthesis, and performance of [68Ga]Ga·DOTA-CMP, which comprises a positron-emitting radioisotope linked to a collagen-mimetic peptide (CMP). This peptide mimics the natural structure of collagen and detects irregular collagen matrices by annealing to damaged collagen triple helices. We assessed the ability of the peptide to detect aberrant lung collagen selectively in a bleomycin-induced mouse model of pulmonary fibrosis using positron emission tomography (PET). [68Ga]Ga·DOTA-CMP PET demonstrated higher and selective uptake in a fibrotic mouse lung compared to controls, minimal background signal in adjacent organs, and rapid clearance via the renal system. These studies suggest that [68Ga]Ga·DOTA-CMP identifies fibrotic lungs and could be useful in the early diagnosis of IPF.

Synchronous percutaneous core-needle biopsy and microwave ablation for stage I non-small cell lung cancer in patients with Idiopathic pulmonary fibrosis: initial experience.

PURPOSE: This study aimed to retrospectively evaluate the safety and feasibility of computed tomography (CT)-guided synchronous percutaneous core-needle biopsy (CNB) and microwave ablation (MWA) for stage I non-small cell lung cancer (NSCLC) in patients with idiopathic pulmonary fibrosis (IPF). METHODS: From January 2019 to January 2023, nineteen stage I NSCLC patients with IPF underwent CT-guided synchronous percutaneous CNB and MWA in this study. The technical success rate, complications, local tumor progression (LTP) and overall survival (OS) were observed, and the effect of synchronous percutaneous CNB and MWA were evaluated. RESULTS: The technical success rate of synchronous percutaneous CNB and MWA was 100%. With a median follow-up time of 20.36 months, the median OS was 25 months (95% CI: 21.79, 28.20). The six-, twelve- and eighteen-month OS rates were 94.73%, 89.47% and 57.89%, respectively. The six-, twelve- and eighteen-month LTP rates were 0%, 10.52% and 31.57%, respectively. Major complications including pneumothorax, bronchopleural fistula and pneumonia occurred in 26.32% (5/19) patients. None of the patients died during the procedure. CONCLUSIONS: According to the results of the current study, CT-guided synchronous percutaneous CNB and MWA appears to be a safe and effective for stage I NSCLC in patients with IPF and providing an alternative therapeutic option for local control of pulmonary malignancy in high-risk patients.

Neutrophil levels correlate with quantitative extent and progression of fibrosis in IPF: results of a single-centre cohort study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with poor prognosis. Clinical studies have demonstrated association between different blood leucocytes and mortality and forced vital capacity (FVC) decline. Here, we question which blood leucocyte levels are specifically associated with progression of fibrosis, measured by accumulation of fibrosis on CT scan using a standardised automated method. METHODS: Using the Computer-Aided Lung Informatics for Pathology Evaluation and Rating CT algorithm, we determined the correlation between different blood leucocytes (<4 months from CT) and total lung fibrosis (TLF) scores, pulmonary vessel volume (PVV), FVC% and transfer factor of lung for carbon monoxide% at baseline (n=171) and with progression of fibrosis (n=71), the latter using multivariate Cox regression. RESULTS: Neutrophils (but not monocyte or lymphocytes) correlated with extent of lung fibrosis (TLF/litre) (r=0.208, p=0.007), PVV (r=0.259, p=0.001), FVC% (r=-0.127, p=0.029) at baseline. For the 71 cases with repeat CT; median interval between CTs was 25.9 (16.8-39.9) months. Neutrophil but not monocyte levels are associated with increase in TLF/litre (HR 2.66, 95% CI 1.35 to 5.25, p=0.005). CONCLUSION: Our study shows that neutrophil rather than monocyte levels correlated with quantifiable increase in fibrosis on imaging of the lungs in IPF, suggesting its relative greater contribution to progression of fibrosis in IPF.

Usefulness of FAPα assessment in bronchoalveolar lavage as a marker of fibrogenesis: results of a preclinical study and first report in patients with idiopathic pulmonary fibrosis.

BACKGROUND: Fibroblast activation protein-α (FAPα) is a marker of activated fibroblasts that can be selectively targeted by an inhibitor (FAPI) and visualised by PET/CT imaging. We evaluated whether the measurement of FAPα in bronchoalveolar lavage fluids (BALF) and the uptake of FAPI by PET/CT could be used as biomarkers of fibrogenesis. METHODS: The dynamics of lung uptake of 18F-labeled FAPI ([18F]FAPI-74) was assessed in the bleomycin mouse model at various time points and using different concentrations of bleomycin by PET/CT. FAPα was measured in BALFs from these bleomycin-treated and control mice. FAPα levels were also assessed in BALFs from controls and patients with idiopathic pulmonary fibrosis (IPF). RESULTS: Bleomycin-treated mice presented a significantly higher uptake of [18F]FAPI-74 during lung fibrinogenesis (days 10 and 16 after instillation) compared to control mice. No significant difference was observed at initial inflammatory phase (3 days) and when fibrosis was already established (28 days). [18F]FAPI-74 tracer was unable to show a dose-response to bleomycin treatment. On the other hand, BALF FAPα levels were steeply higher in bleomycin-treated mice at day 10 and a significant dose-response effect was observed. Moreover, FAPα levels were strongly correlated with lung fibrosis as measured by the modified Aschroft histological analysis, hydroxyproline and the percentage of weight loss. Importantly, higher levels of FAPα were observed in IPF patients where the disease was progressing as compared to stable patients and controls. Moreover, patients with FAPα BALF levels higher than 192.5 pg/mL presented a higher risk of progression, transplantation or death compared to patients with lower levels. CONCLUSIONS: Our preclinical data highlight a specific increase of [18F]FAPI-74 lung uptake during the fibrotic phase of the bleomycin murine model. The measurement of FAPα in BALF appears to be a promising marker of the fibrotic activity in preclinical models of lung fibrosis and in IPF patients. Further studies are required to confirm the role of FAPα in BALF as biomarker of IPF activity and assess the relationship between FAPα levels in BALF and [18F]FAPI-74 uptake on PET/CT in patients with fibrotic lung disease.

Ultrasound evaluation of diaphragmatic function in patients with idiopathic pulmonary fibrosis: a retrospective observational study.

INTRODUCTION: The diaphragm function assessed by ultrasound has been well-studied in COPD, asthma, and intensive care. However, there are only a few studies on diffuse interstitial lung disease, while dyspnea and quality of life are major issues in the management that may depend on the diaphragm. METHODS: We retrospectively included idiopathic pulmonary fibrosis (IPF) patients followed in our center (Marseille, France) between January 2020 and February 2023 who underwent diaphragmatic ultrasound. Our objectives were to describe the diaphragmatic function of IPFs compared to healthy controls and to correlate with clinical, functional, and lung density on CT-scan. RESULTS: 24 IPF patients and 157 controls were included. The diaphragmatic amplitude in IPF was increased at rest (median of 2.20 cm vs 1.88 cm on the right, p < 0.007, and 2.30 cm vs 1.91 cm on the left, p < 0.03, in IPF and controls respectively) and decreased in deep breathing (median of 4.85 cm vs 5.45 cm on the right, p < 0.009, and 5.10 cm vs 5.65 cm on the left, p < 0.046, in IPF and controls respectively). Diaphragmatic thickness was significantly reduced at rest on the right side (median of 1.75 mm vs 2.00 mm, p < 0.02, in IPF and controls respectively) and in deep breathing on both sides compared to controls (mean of 3.82 mm vs 4.15 mm on the right, p < 0.02, and 3.53 mm vs 3.94 mm, on the left, p < 0.009, in IPF and controls respectively). Diaphragmatic amplitude in deep breathing was moderate to strongly correlated with FVC, DLCO, and 6MWT and negatively correlated with the dyspnea and lung density on CT scan. CONCLUSION: The diaphragmatic amplitude and thickness were impaired in IPF compared to controls. Diaphragmatic amplitude is the parameter best correlated with clinical, functional, and lung density criteria. Further studies are needed to determine if diaphragmatic amplitude can be a prognostic factor in IPF.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.