Contrast enhanced longitudinal changes observed in an experimental bleomycin-induced lung fibrosis rat model by radial DCE-MRI at 9.4T.

Identifying biomarkers in fibrotic lung disease is key for early anti-fibrotic intervention. Dynamic contrast-enhanced (DCE) MRI offers valuable perfusion-related insights in fibrosis but adapting human MRI methods to rodents poses challenges. Here, we explored these translational challenges for the inflammatory and fibrotic phase of a bleomycin lung injury model in rats. Eleven male Sprague-Dawley rats received a single intratracheal dose of bleomycin (1000iU), four control rats received saline. Imaging was performed on days 7 and 28 post-induction. Ultra-short echo time imaging was used to image the lung for 7 minutes after which Clariscan was injected intravenously. Lung signal changes were measured for an additional 21 minutes. Images were reconstructed with a sliding-window approach, providing a temporal resolution of 10 seconds per image. After imaging on day 28, animals were euthanized, and lungs were collected for histology. Bleomycin-exposed rats initially exhibited reduced body weight, recovering to control levels after 20 days. Lung volume increased in bleomycin animals from 4.4±0.9 ml in controls to 5.5±0.5 ml and 6.5±1.2 ml on day 7 and 28. DCE-MRI showed no change of initial gradient of relative enhancement in the curves between controls and bleomycin animals on day 7 and 28 post-induction. On day 7, the DCE-MRI washout phase in bleomycin animals had higher signals than the saline group and than observed at a later time point. Lung pixels were binned in 7 enhancement classes. On day 28, the size of low relative enhancement bins almost doubled in volume compared to controls and animals on day 7 post-induction. Histology on day 28 suggests that findings could be explained by changes in lung tissue density due to lung volume increase. Adapting this clinical MRI method to rodents at 9.4T remains a challenge. Future studies may benefit from lower field strength MRI combined with higher temporal resolution DCE-MRI.

Establishment of a stem cell administration imaging method in bleomycin-induced pulmonary fibrosis mouse models.

Pulmonary fibrosis is a progressive disease caused by interstitial inflammation. Treatments are extremely scarce; therapeutic drugs and transplantation therapies are not widely available due to cost and a lack of donors, respectively. Recently, there has been a high interest in regenerative medicine and exponential advancements in stem cell-based therapies have occurred. However, a sensitive imaging technique for investigating the in vivo dynamics of transplanted stem cells has not yet been established and the mechanisms of stem cell-based therapy remain largely unexplored. In this study, we administered mouse adipose tissue-derived mesenchymal stem cells (mASCs) labeled with quantum dots (QDs; 8.0 nM) to a mouse model of bleomycin-induced pulmonary fibrosis in an effort to clarify the relationship between in vivo dynamics and therapeutic efficacy. These QD-labeled mASCs were injected into the trachea of C57BL/6 mice seven days after bleomycin administration to induce fibrosis in the lungs. The therapeutic effects and efficacy were evaluated via in vivo/ex vivo imaging, CT imaging, and H&E staining of lung sections. The QD-labeled mASCs remained in the lungs longer and suppressed fibrosis. The 3D imaging results showed that the transplanted cells accumulated in the peripheral and fibrotic regions of the lungs. These results indicate that mASCs may prevent fibrosis. Thus, QD labeling could be a suitable and sensitive imaging technique for evaluating in vivo kinetics in correlation with the efficacy of cell therapy.

A branched polymer-based agent for efficient and precise targeting of fibrosis diseases by magnetic resonance imaging.

Herein, we synthesized and characterized gadolinium-based hyperbranched polymers, POADGd and PODGd, through RAFT polymerization as magnetic resonance imaging (MRI) contrast agents for detecting fibrosis. POADGd and PODGd contain biocompatible short-chain OEGMA to prolong blood circulation, and they can be decomposed in response to ROS after MRI examination to prevent potential accumulation. The relaxivities of POADGd and PODGd are 9.81 mM-1 s-1 and 9.58 mM-1 s-1 respectively, which are significantly higher than that of DTPA-Gd, a clinically used agent (3.74 mM-1 s-1). In comparison with PODGd, POADGd can specifically target allysine in fibrosis tissues through its oxyamine groups. Therefore, it displays a sharp spatial resolution and a high signal-to-noise ratio in the liver and lung fibrosis tissue at a field strength of 3.0 T or 7.0 T, and the morphology of these fibrosis tissues is accurately delineated. Our MRI diagnosis results based on POADGd are highly aligned with those from pathological examinations, while MRI diagnosis could avoid invasive biopsy. In addition, POADGd shows excellent biosafety and low toxicity. Therefore, POADGd could be applied to non-invasively and accurately diagnose liver and lung fibrosis diseases.

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Radioproteomics stratifies molecular response to antifibrotic treatment in pulmonary fibrosis.

Antifibrotic therapy with nintedanib is the clinical mainstay in the treatment of progressive fibrosing interstitial lung disease (ILD). High-dimensional medical image analysis, known as radiomics, provides quantitative insights into organ-scale pathophysiology, generating digital disease fingerprints. Here, we performed an integrative analysis of radiomic and proteomic profiles (radioproteomics) to assess whether changes in radiomic signatures can stratify the degree of antifibrotic response to nintedanib in (experimental) fibrosing ILD. Unsupervised clustering of delta radiomic profiles revealed 2 distinct imaging phenotypes in mice treated with nintedanib, contrary to conventional densitometry readouts, which showed a more uniform response. Integrative analysis of delta radiomics and proteomics demonstrated that these phenotypes reflected different treatment response states, as further evidenced on transcriptional and cellular levels. Importantly, radioproteomics signatures paralleled disease- and drug-related biological pathway activity with high specificity, including extracellular matrix (ECM) remodeling, cell cycle activity, wound healing, and metabolic activity. Evaluation of the preclinical molecular response-defining features, particularly those linked to ECM remodeling, in a cohort of nintedanib-treated fibrosing patients with ILD, accurately stratified patients based on their extent of lung function decline. In conclusion, delta radiomics has great potential to serve as a noninvasive and readily accessible surrogate of molecular response phenotypes in fibrosing ILD. This could pave the way for personalized treatment strategies and improved patient outcomes.

Simultaneous Positron Emission Tomography and Molecular Magnetic Resonance Imaging of Cardiopulmonary Fibrosis in a Mouse Model of Left Ventricular Dysfunction.

BACKGROUND: Aging-associated left ventricular dysfunction promotes cardiopulmonary fibrogenic remodeling, Group 2 pulmonary hypertension (PH), and right ventricular failure. At the time of diagnosis, cardiac function has declined, and cardiopulmonary fibrosis has often developed. Here, we sought to develop a molecular positron emission tomography (PET)-magnetic resonance imaging (MRI) protocol to detect both cardiopulmonary fibrosis and fibrotic disease activity in a left ventricular dysfunction model. METHODS AND RESULTS: Left ventricular dysfunction was induced by transverse aortic constriction (TAC) in 6-month-old senescence-accelerated prone mice, a subset of mice that received sham surgery. Three weeks after surgery, mice underwent simultaneous PET-MRI at 4.7 T. Collagen-targeted PET and fibrogenesis magnetic resonance (MR) probes were intravenously administered. PET signal was computed as myocardium- or lung-to-muscle ratio. Percent signal intensity increase and Δ lung-to-muscle ratio were computed from the pre-/postinjection magnetic resonance images. Elevated allysine in the heart (P=0.02) and lungs (P=0.17) of TAC mice corresponded to an increase in myocardial magnetic resonance imaging percent signal intensity increase (P<0.0001) and Δlung-to-muscle ratio (P<0.0001). Hydroxyproline in the heart (P<0.0001) and lungs (P<0.01) were elevated in TAC mice, which corresponded to an increase in heart (myocardium-to-muscle ratio, P=0.02) and lung (lung-to-muscle ratio, P<0.001) PET measurements. Pressure-volume loop and echocardiography demonstrated adverse left ventricular remodeling, function, and increased right ventricular systolic pressure in TAC mice. CONCLUSIONS: Administration of collagen-targeted PET and allysine-targeted MR probes led to elevated PET-magnetic resonance imaging signals in the myocardium and lungs of TAC mice. The study demonstrates the potential to detect fibrosis and fibrogenesis in cardiopulmonary disease through a dual molecular PET-magnetic resonance imaging protocol.

Lung quantitative ultrasound to stage and monitor interstitial lung diseases.

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine quantitative ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate (p = 1e-4 for edema and p = 3e-7 for fibrosis) to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

Noninvasive assessment of the lung inflammation-fibrosis axis by targeted imaging of CMKLR1.

Precision management of fibrotic lung diseases is challenging due to their diverse clinical trajectories and lack of reliable biomarkers for risk stratification and therapeutic monitoring. Here, we validated the accuracy of CMKLR1 as an imaging biomarker of the lung inflammation-fibrosis axis. By analyzing single-cell RNA sequencing datasets, we demonstrated CMKLR1 expression as a transient signature of monocyte-derived macrophages (MDMφ) enriched in patients with idiopathic pulmonary fibrosis (IPF). Consistently, we identified MDMφ as the major driver of the uptake of CMKLR1-targeting peptides in a murine model of bleomycin-induced lung fibrosis. Furthermore, CMKLR1-targeted positron emission tomography in the murine model enabled quantification and spatial mapping of inflamed lung regions infiltrated by CMKLR1-expressing macrophages and emerged as a robust predictor of subsequent lung fibrosis. Last, high CMKLR1 expression by bronchoalveolar lavage cells identified an inflammatory endotype of IPF with poor survival. Our investigation supports the potential of CMKLR1 as an imaging biomarker for endotyping and risk stratification of fibrotic lung diseases.

Enhanced Detection of Early Pulmonary Fibrosis Disease Using 68Ga-FAPI-LM3 PET.

Early detection of pulmonary fibrosis is a critical yet insufficiently met clinical necessity. This study evaluated the effectiveness of FAPI-LM3, a 68Ga-radiolabeled heterobivalent molecular probe that targets fibroblast activating protein (FAP) and somatostatin receptor 2 (SSTR2), in the early detection of pulmonary fibrosis, leveraging its potential for early disease identification. A bleomycin-induced early pulmonary fibrosis model was established in C57BL/6 mice for 7 days. FAP and SSTR2 expression levels were quantitatively assessed in human idiopathic pulmonary fibrosis lung tissue samples and bleomycin-treated mouse lung tissues by using western blotting, real-time quantitative PCR (RT-qPCR), and immunofluorescence techniques. The diagnostic performance of FAPI-LM3 was investigated by synthesizing monomeric radiotracers 68Ga-FAPI-46 and 68Ga-DOTA-LM3 alongside the heterobivalent probe 68Ga-FAPI-LM3. These imaging radiopharmaceuticals were used in small-animal PET to compare their uptake in fibrotic and normal lung tissues. Results indicated significant upregulation of FAP and SSTR2 at both RNA and protein levels in fibrotic lung tissues compared with that in normal controls. PET imaging demonstrated significantly enhanced uptake of the 68Ga-FAPI-LM3 probe in fibrotic lung tissues, with superior visual effects compared to monomeric tracers. At 60 min postinjection, early stage fibrotic tissues (day 7) demonstrated low-to-medium uptake of monomeric probes, including 68Ga-DOTA-LM3 (0.45 ± 0.04% ID/g) and 68Ga-FAPI-46 (0.78 ± 0.09% ID/g), whereas the uptake of the heterobivalent probe 68Ga-FAPI-LM3 (1.90 ± 0.10% ID/g) was significantly higher in fibrotic lesions than in normal lung tissue. Blockade experiments confirmed the specificity of 68Ga-FAPI-LM3 uptake, which was attributed to synergistic targeting of FAP and SSTR2. This study demonstrates the potential of 68Ga-FAPI-LM3 for early pulmonary fibrosis detection via molecular imaging, offering significant benefits over monomeric tracers 68Ga-FAPI-46 and 68Ga-DOTA-LM3. This strategy offers new possibilities for noninvasive and precise early detection of pulmonary fibrosis.

Micro-CT-assisted identification of the optimal time-window for antifibrotic treatment in a bleomycin mouse model of long-lasting pulmonary fibrosis.

Idiopathic Pulmonary Fibrosis (IPF) is a debilitating and fatal lung disease characterized by the excessive formation of scar tissue and decline of lung function. Despite extensive research, only two FDA-approved drugs exist for IPF, with limited efficacy and relevant side effects. Thus, there is an urgent need for new effective therapies, whose discovery strongly relies on IPF animal models. Despite some limitations, the Bleomycin (BLM)-induced lung fibrosis mouse model is widely used for antifibrotic drug discovery and for investigating disease pathogenesis. The initial acute inflammation triggered by BLM instillation and the spontaneous fibrosis resolution that occurs after 3 weeks are the major drawbacks of this system. In the present study, we applied micro-CT technology to a longer-lasting, triple BLM administration fibrosis mouse model to define the best time-window for Nintedanib (NINT) treatment. Two different treatment regimens were examined, with a daily NINT administration from day 7 to 28 (NINT 7-28), and from day 14 to 28 (NINT 14-28). For the first time, we automatically derived both morphological and functional readouts from longitudinal micro-CT. NINT 14-28 showed significant effects on morphological parameters after just 1 week of treatment, while no modulations of these biomarkers were observed during the preceding 7-14-days period, likely due to persistent inflammation. Micro-CT morphological data evaluated on day 28 were confirmed by lung histology and bronchoalveolar lavage fluid (BALF) cells; Once again, the NINT 7-21 regimen did not provide substantial benefits over the NINT 14-28. Interestingly, both NINT treatments failed to improve micro-CT-derived functional parameters. Altogether, our findings support the need for optimized protocols in preclinical studies to expedite the drug discovery process for antifibrotic agents. This study represents a significant advancement in pulmonary fibrosis animal modeling and antifibrotic treatment understanding, with the potential for improved translatability through the concurrent structural-functional analysis offered by longitudinal micro-CT.

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

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.

Microscopic computed tomography with AI-CNN-powered image analysis: the path to phenotype bleomycin-induced lung injury.

Bleomycin (BLM)-induced lung injury in mice is a valuable model for investigating the molecular mechanisms that drive inflammation and fibrosis and for evaluating potential therapeutic approaches to treat the disease. Given high variability in the BLM model, it is critical to accurately phenotype the animals in the course of an experiment. In the present study, we aimed to demonstrate the utility of microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation for rapid phenotyping of BLM mice. µCT was performed in freely breathing C57BL/6J mice under isoflurane anesthesia on days 7 and 21 after BLM administration. Terminal invasive lung function measurement and histological assessment of the left lung collagen content were conducted as well. µCT image analysis demonstrated gradual and time-dependent development of lung injury as evident by alterations in the lung density, air-to-tissue volume ratio, and lung aeration in mice treated with BLM. The right and left lung were unequally affected. µCT-derived parameters such as lung density, air-to-tissue volume ratio, and nonaerated lung volume correlated well with the invasive lung function measurement and left lung collagen content. Our study demonstrates the utility of AI-CNN-powered µCT image analysis for rapid and accurate phenotyping of BLM mice in the course of disease development and progression.NEW & NOTEWORTHY Microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation is a rapid and powerful tool for noninvasive phenotyping of bleomycin mice over the course of the disease. This, in turn, allows earlier and more reliable identification of therapeutic effects of new drug candidates, ultimately leading to the reduction of unnecessary procedures in animals in pharmacological research.

[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.

Improved pulmonary function and exercise tolerance despite persistent pulmonary fibrosis over 1 year after severe COVID-19 infection.

We conducted a prospective single-centre cohort study of 104 multi-ethnic severe COVID-19 survivors from the first wave of the pandemic 15 months after hospitalisation. Of those who were assessed at 4 and 15 months, improvement of ground glass opacities correlated with worsened fibrotic reticulations. Despite a high prevalence of fibrotic patterns (64%), pulmonary function, grip strength, 6 min walk distance and frailty normalised. Overall, dyspnoea, cough and exhaustion did not improve and were not correlated with pulmonary function or radiographic fibrosis at 15 months, suggesting non-respiratory aetiologies. Monitoring persistent, and often subclinical, fibrotic interstitial abnormalities will be needed to determine their potential for future progression.

Genetic Testing Goes Beyond Imaging and Histological Evaluation in Pleuroparenchymal Fibroelastosis.

BACKGROUND: Lung biopsy remains the gold standard in the diagnosis of fibrotic interstitial lung disease (F-ILD), but there is a growing appreciation of the role of pathogenic gene variants in telomere and surfactant protein genes, especially in familial pulmonary fibrosis (FPF). Pleuroparenchymal fibroelastosis (PPFE) is a rare disease that can coexist with different patterns of F-ILD, including FPF. It can be progressive and often leads to respiratory failure and death. This study tested the hypothesis that genetic testing goes beyond radiological and histological findings in PPFE and other F-ILD further informing clinical decision-making for patients and affected family members by identifying pathological gene variants in telomere and surfactant protein genes. METHODS: This is a retrospective review of 70 patients with F-ILD in the setting of FPF or premature lung fibrosis. Six out of 70 patients were diagnosed with PPFE based on radiological or histological characteristics. All patients underwent telomere length evaluation in peripheral blood by Flow-FISH or genetic testing using a customized exome-based panel that included telomere and surfactant protein genes associated with lung fibrosis. RESULTS: Herein, we identified six individuals where radiographic or histopathological analyses of PPFE were linked with telomere biology disorders (TBD) or variants in surfactant protein genes. Each case involved individuals with either personal early-onset lung fibrosis or a family history of the disease. Assessments of telomere length and genetic testing offered insights beyond traditional radiological and histopathological evaluations. CONCLUSION: Detecting anomalies in TBD-related or surfactant protein genes can significantly refine the diagnosis and treatment strategies for individuals with PPFE and other F-ILD.

Fibrotic lung diseases in children.

In children, pulmonary fibrosis (PF) is an extremely unusual entity that can be observed in some types of interstitial lung disease (ILD). Defining whether ILD is accompanied by PF is important for targeted therapy. Algorithm for the diagnosis of PF in children is not clearly established. Besides, the clinical, radiological, and histological definitions commonly used to diagnose particularly the cases of idiopathic PF in adult patients, is not applicable to pediatric cases. However, a few studies conducted in children offer good exemplary diagnostic approach to fibrosing ILD. Thorax high resonance computed tomography and/or lung biopsy scanning can provide valuable information about PF. Another issue that has not been clearly established is when to start antifibrotic treatment in pediatric patients with PF. The objective of this current review is to provide a comprehensive overview of pediatric PF by drawing upon adult research, particularly focusing on the areas of uncertainty.