Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study.

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests. METHODS: We retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls. RESULTS: ROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85-0.96). Additionally, with F% = 4.05% as the threshold, the Youden's J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = - 0.571, - 0.520, - 0.521, - 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = - 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001). CONCLUSIONS: Quantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression.

IA-Body Composition CT at T12 in Idiopathic Pulmonary Fibrosis: Diagnosing Sarcopenia and Correlating with Other Morphofunctional Assessment Techniques.

BACKGROUND: Body composition (BC) techniques, including bioelectrical impedance analysis (BIVA), nutritional ultrasound® (NU), and computed tomography (CT), can detect nutritional diagnoses such as sarcopenia (Sc). Sc in idiopathic pulmonary fibrosis (IPF) is associated with greater severity and lower survival. Our aim was to explore the correlation of BIVA, NU and functional parameters with BC at T12 level CT scans in patients with IPF but also its relationship with degree of Sc, malnutrition and mortality. METHODS: This bicentric cross-sectional study included 60 IPF patients (85.2% male, 70.9 ± 7.8 years). Morphofunctional assessment (MFA) techniques included BIVA, NU, CT at T12 level (T12-CT), handgrip strength, and timed up and go. CT data were obtained using FocusedON®. Statistical analysis was conducted using JAMOVI version 2.3.22 to determine the cutoff points for Sc in T12-CT and to analyze correlations with other MFA techniques. RESULTS: the cutoff for muscle area in T12-CT was ≤77.44 cm2 (area under the curve (AUC) = 0.734, sensitivity = 41.7%, specificity = 100%). The skeletal muscle index (SMI_T12CT) cutoff was ≤24.5 cm2/m2 (AUC = 0.689, sensitivity = 66.7%, specificity = 66.7%). Low SMI_T12CT exhibited significantly reduced median survival and higher risk of mortality compared to those with normal muscle mass (SMI cut off ≥ 28.8 cm/m2). SMI_T12CT was highly correlated with body cell mass from BIVA (r = 0.681) and rectus femoris cross-sectional area (RF-CSA) from NU (r = 0.599). Cronbach's α for muscle parameters across different MFA techniques and CT was 0.735, confirming their validity for evaluating muscle composition. CONCLUSIONS: T12-CT scan is a reliable technique for measuring low muscle mass in patients with IPF, specifically when the L3 vertebrae are not captured. An SMI value of <28.8 is a good predictor of low lean mass and 12-month mortality in IPF patients.

Variability of radiological and clinical features in cases with usual interstitial pneumonia without honeycombing.

BACKGROUND: Usual interstitial pneumonia (UIP) cases without honeycombing (possible UIP) included various CT features and was often difficult to diagnose. PURPOSE: This study aimed to classify the cases with possible UIP on CT features using cluster analysis and evaluate the features of subsets of participants and the correlation of prognosis. MATERIALS AND METHODS: The study included 85 patients with possible UIP in the 2011 idiopathic pulmonary fibrosis (IPF) guideline with radiological diagnosis. All cases underwent surgical biopsies and were diagnosed by multidisciplinary discussion (MDD) from the nationwide registry in Japan. The readers evaluated pulmonary opacity, nodules, cysts, and predominant distribution which were reclassified by IPF guidelines in 2018. Additionally, cases were classified into four groups by cluster analysis based on CT findings. The differences in survival among IPF classification and the clusters were evaluated. RESULTS: Cases were diagnosed as IPF (n = 55), NSIP (n = 4), unclassifiable (n = 23), and others (n = 3) by MDD. Cluster analysis revealed 4 clusters by CT features (n = 47, 16, 19 and 3, respectively). Cluster 1 had fewer lesions overall. Cluster 2 have many pure ground-glass opacities and ground-glass opacities with reticulation. Cluster 3 had many reticular opacities and nodules with few lower predominant distributions. Cluster 4 was characterized by peribronchovascular consolidation.The mean survival time of cluster 1 (4518 days) was significantly better than cluster 2, 3, and 4 (1843, 2196, and 1814 days, respectively) (p = 0.03). CONCLUSION: In conclusion, UIP without honeycombing included various CT patterns and MDD diagnoses. Significangly differences in prognosis were observed among clusters classified by CT findings.

A nitroreductase responsive probe for early diagnosis of pulmonary fibrosis disease.

Idiopathic pulmonary fibrosis (IPF) is a serious interstitial lung disease. However, the definitive diagnosis of IPF is impeded by the limited capabilities of current diagnostic methods, which may fail to capture the optimal timing for treatment. The main goal of this study is to determine the feasibility of a nitroreductase (NTR) responsive probe, 18F-NCRP, for early detection and deterioration monitoring of IPF. 18F-NCRP was obtained with high radiochemical purity (>95 %). BLM-injured mice were established by intratracheal instillation with bleomycin (BLM) and characterized through histological analysis. Longitudinal PET/CT imaging, biodistribution study and in vitro autoradiography were performed. The correlations between the uptake of 18F-NCRP and mean lung density (tested by CT), as well as histopathological characteristics were analyzed. In PET imaging study, 18F-NCRP exhibited promising efficacy in monitoring the progression of IPF, which was earlier than CT. The ratio of uptake in BLM-injured lung to control lung increased from 1.4-fold on D15 to 2.2-fold on D22. Biodistribution data showed a significant lung uptake of 18F-NCRP in BLM-injured mice. There was a strong positive correlation between the 18F-NCRP uptake in the BLM-injured lungs and the histopathological characteristics. Given that, 18F-NCRP PET imaging of NTR, a promising biomarker for investigating the underlying pathogenic mechanism of IPF, is attainable as well as desirable, which might lay the foundation for establishing an NTR-targeted imaging evaluation system of IPF.

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

PURPOSE: The extent of honeycombing and reticulation predict the clinical prognosis of IPF. Emphysema, consolidation, and ground glass opacity are visible in HRCT scans. To date, there have been few comprehensive studies that have used these parameters. We conducted automated quantitative analysis to identify predictive parameters for clinical outcomes and then grouped the subjects accordingly. METHODS: CT images were obtained while patients held their breath at full inspiration. Parameters were analyzed using an automated lung texture quantification system. Cluster analysis was conducted on 159 IPF patients and clinical profiles were compared between clusters in terms of survival. RESULTS: Kaplan-Meier analysis revealed that survival rates declined as fibrosis, reticulation, honeycombing, consolidation, and emphysema scores increased. Cox regression analysis revealed that reticulation had the most significant impact on survival rate, followed by honeycombing, consolidation, and emphysema scores. Hierarchical and K-means cluster analyses revealed 3 clusters. Cluster 1 (n = 126) with the lowest values for all parameters had the longest survival duration, and relatively-well preserved FVC and DLCO. Cluster 2 (n = 15) with high reticulation and consolidation scores had the lowest FVC and DLCO values with a predominance of female, while cluster 3 (n = 18) with high honeycombing and emphysema scores predominantly consisted of male smokers. Kaplan-Meier analysis revealed that cluster 2 had the lowest survival rate, followed by cluster 3 and cluster 1. CONCLUSION: Automated quantitative CT analysis provides valuable information for predicting clinical outcomes, and clustering based on these parameters may help identify the high-risk group for management.

Validation of a computed tomography diagnostic model for differentiating fibrotic hypersensitivity pneumonitis from idiopathic pulmonary fibrosis.

BACKGROUND: The diagnosis of fibrotic hypersensitivity pneumonitis (fHP) from other interstitial lung diseases, particularly idiopathic pulmonary fibrosis (IPF), is often difficult. This study aimed to examine computed tomography (CT) findings that were useful for differentiating between fHP and IPF and to develop and validate a radiological diagnostic model. METHODS: In this study, 246 patients (fHP, n = 104; IPF, n = 142) from two institutions were included and randomly divided into the test (n = 164) and validation (n = 82) groups (at a 2:1 ratio). Three radiologists evaluated CT findings, such as pulmonary fibrosis, small airway disease, and predominant distribution, and compared them between fHP and IPF using binomial logistic regression and multivariate analysis. A prognostic model was developed from the test group and validated with the validation group. RESULTS: Ground-glass opacity (GGO) with traction bronchiectasis (TB), honeycombing, hypoattenuation area, three-density pattern, diffuse craniocaudal distribution, peribronchovascular opacities in the upper lung, and random distribution were more common in fHP than in IPF. In multivariate analysis, GGO with TB, peribronchovascular opacities in the upper lung, and random distribution were significant features. The area under the curve of the fHP diagnostic model with the three aforementioned CT features was 0.733 (95% confidence interval [CI], 0.655-0.811, p < 0.001) in the test group and 0.630 (95% CI, 0.504-0.755, p < 0.047) in the validation group. CONCLUSION: GGO with TB, peribronchovascular opacities in the upper lung, and random distribution were important CT features for differentiating fHP from IPF.

Identification of risk factors for acute exacerbation of idiopathic pulmonary fibrosis based on baseline high-resolution computed tomography: a prospective observational study.

BACKGROUND: This study aimed to investigate risk factors for acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) based on baseline high-resolution computed tomography (HRCT). METHODS: This prospective observational study enrolled patients with IPF treated at the General Hospital of Ningxia Medical University between January 2019 and January 2021. HRCT-derived quantitative parameters at baseline were analyzed. RESULTS: A total of 102 patients [92 (90.2%) males with a mean age of 67 years] with IPF were included, with a median follow-up of 32 (24-40.5) months. AE occurred in 30 (29.4%) IPF patients. Multivariable logistic regression analysis identified Doppler transthoracic echocardiography suggestive of pulmonary hypertension (PH) (13.43; 95% CI: 4.18-41.09; P < 0.001), honeycombing (OR 1.08; 95% CI: 1.02-1.14; P = 0.013), and whole lung volume (OR 0.99; 95% CI: 0.99-1.00; P = 0.037) as independent risk factors for AE-IPF. The combination of PH, honeycombing, whole lung volume, and the percentage of predicted forced vital capacity (FVC% pred) showed a high area under the curve from receiver operating characteristic curves of 0.888, with a sensitivity of 90% and specificity of 78%. CONCLUSIONS: This study emphasizes that quantitative CT parameters (honeycombing, whole lung volume) may serve as risk factors for AE-IPF. The combination of honeycombing, whole lung volume, FVC% pred, and PH may aid in predicting AE-IPF.

Clinical, radiological and histopathological features of patients with familial pulmonary fibrosis.

BACKGROUND: In familial pulmonary fibrosis (FPF) at least two biological relatives are affected. Patients with FPF have diverse clinical features. RESEARCH QUESTION: We aimed to characterize demographic and clinical features, re-evaluate high-resolution computed tomography (HRCT) scans and histopathology of surgical lung biopsies, assess survival and investigate the suitability of risk prediction models for FPF patients. STUDY DESIGN: A retrospective cohort study. METHODS: FPF data (n = 68) were collected from the medical records of Oulu University Hospital (OUH) and Oulaskangas District Hospital between 1 Jan 2000 and 11 Jan 2023. The inclusion criterion was pulmonary fibrosis (PF) (ICD 10-code J84.X) and at least one self-reported relative with PF. Clinical information was gathered from hospital medical records. HRCT scans and histology were re-evaluated. RESULTS: Thirty-seven (54.4%) of the patients were men, and 31 (45.6%) were women. The mean ages of the women and men were 68.6 and 61.7 years, respectively (p = 0.003). Thirty-seven (54.4%) patients were nonsmokers. The most common radiological patterns were usual interstitial pneumonia (UIP) (51/75.0%), unclassifiable (8/11.8%) and nonspecific interstitial pneumonia (NSIP) (3/4.4%). Pleuroparenchymal fibroelastosis (PPFE) was observed as a single or combined pattern in 13.2% of the patients. According to the 2022 guidelines for idiopathic pulmonary fibrosis (IPF), the patients were categorized as UIP (31/45.6%), probable UIP (20/29.4%), indeterminate for UIP (7/10.3%) or alternative diagnosis (10/14.7%). The histopathological patterns were UIP (7/41.2%), probable UIP (1/5.9%), indeterminate for UIP (8/47.2%) and alternative diagnosis (1/5.9%). Rare genetic variants were found in 9 patients; these included telomerase reverse transcriptase (TERT, n = 6), telomerase RNA component (TERC, n = 2) and regulator of telomere elongation helicase 1 (RTEL1, n = 1). Half of the patients died (n = 29) or underwent lung transplantation (n = 5), with a median survival of 39.9 months. The risk prediction models composite physiology index (CPI), hazard ratio (HR) 1.07 (95.0% CI 1.04-1.10), and gender-age-physiology index (GAP) stage I predicted survival statistically significantly (p<0.001) compared to combined stages II and III. CONCLUSIONS: This study confirmed the results of earlier studies showing that FPF patients' radiological and histopathological patterns are diverse. Moreover, radiological and histological features revealed unusual patterns and their combinations.

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.

Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis.

PURPOSE OF REVIEW: To discuss the most recent applications of radiological imaging, from conventional to quantitative, in the setting of idiopathic pulmonary fibrosis (IPF) diagnosis. RECENT FINDINGS: In this article, current concepts on radiological diagnosis of IPF, from high-resolution computed tomography (CT) to other imaging modalities, are reviewed. In a separate section, advances in quantitative CT and development of novel imaging biomarkers, as well as current limitations and future research trends, are described. SUMMARY: Radiological imaging in IPF, particularly quantitative CT, is an evolving field which holds promise in the future to allow for an increasingly accurate disease assessment and prognostication of IPF patients. However, further standardization and validation studies of alternative imaging applications and quantitative biomarkers are needed.

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.

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (CT) imaging. We hypothesized that Fibresolve is a useful predictor of mortality in interstitial lung diseases (ILD). METHODS: Fibresolve was previously validated in a multi-site >500-patient dataset. In this analysis, we assessed the usefulness of Fibresolve to predict mortality in a subset of 228 patients with IPF and other ILDs in whom follow up data was available. We applied Cox regression analysis adjusting for the Gender, Age, and Physiology (GAP) score and for other known predictors of mortality in IPF. We also analyzed the role of Fibresolve as tertiles adjusting for GAP stages. RESULTS: During a median follow-up of 2.8 years (range 5 to 3434 days), 89 patients died. After adjusting for GAP score and other mortality risk factors, the Fibresolve score significantly predicted the risk of death (HR: 7.14; 95% CI: 1.31-38.85; p = 0.02) during the follow-up period, as did forced vital capacity and history of lung cancer. After adjusting for GAP stages and other variables, Fibresolve score split into tertiles significantly predicted the risk of death (p = 0.027 for the model; HR 1.37 for 2nd tertile; 95% CI: 0.77-2.42. HR 2.19 for 3rd tertile; 95% CI: 1.22-3.93). CONCLUSIONS: The machine learning classifier Fibresolve demonstrated to be an independent predictor of mortality in ILDs, with prognostic performance equivalent to GAP based solely on CT images.

A quantitative analysis of progressive fibrosing interstitial lung disease on computed tomography for the assessment of decreased vital capacity.

BACKGROUND: The results of a quantitative analysis of computed tomography (CT) of interstitial lung disease (ILD) using a computer-aided detection (CAD) technique were correlated with the results of pulmonary function tests. PURPOSE: To evaluate the correlation between a quantitative analysis of CT of progressive fibrosing interstitial lung disease (PF-ILD) including idiopathic pulmonary fibrosis (IPF) and non-IPF, which can manifest progressive pulmonary fibrosis and the vital capacity (VC), and to identify indicators for the assessment of a decreased VC. MATERIAL AND METHODS: A total of 73 patients (46 patients with IPF and 27 patients with non-IPF) were included in this study. Associations between the quantitative analysis of CT and the %VC using a CAD software program were investigated using Spearman's rank correlation and a logistic regression analysis. The appropriate cutoff vale for predicting a decreased VC was determined (%VC <80) and the area under the curve (AUC) was calculated. RESULTS: A multiple logistic regression analysis showed that the total extent of interstitial pneumonia on CT was a significant indicator of a decreased VC (P = 0.0001; odds ratio [OR]=1.15; 95% confidence interval [CI]=1.06-1.27 in IPF and P = 0.0025; OR=1.16; 95% CI=1.03-1.30 in non-IPF). The cutoff values of the total extent of interstitial pneumonia in IPF and non-IPF for predicting a decreased VC were determined to be 23.3% and 21.5%, and the AUCs were 0.83 and 0.91, respectively. CONCLUSION: A quantitative analysis of CT of PF-ILD using a CAD software program could be useful for predicting a decreased VC.

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 Small Airway Abnormalities Identified in Early Idiopathic Pulmonary Fibrosis In Vivo Using Endobronchial Optical Coherence Tomography.

Rationale: Idiopathic pulmonary fibrosis (IPF) affects the subpleural lung but is considered to spare small airways. Micro-computed tomography (micro-CT) studies demonstrated small airway reduction in end-stage IPF explanted lungs, raising questions about small airway involvement in early-stage disease. Endobronchial optical coherence tomography (EB-OCT) is a volumetric imaging modality that detects microscopic features from subpleural to proximal airways. Objectives: In this study, EB-OCT was used to evaluate small airways in early IPF and control subjects in vivo. Methods: EB-OCT was performed in 12 subjects with IPF and 5 control subjects (matched by age, sex, smoking history, height, and body mass index). Subjects with IPF had early disease with mild restriction (FVC: 83.5% predicted), which was diagnosed per current guidelines and confirmed by surgical biopsy. EB-OCT volumetric imaging was acquired bronchoscopically in multiple, distinct, bilateral lung locations (total: 97 sites). IPF imaging sites were classified by severity into affected (all criteria for usual interstitial pneumonia present) and less affected (some but not all criteria for usual interstitial pneumonia present). Bronchiole count and small airway stereology metrics were measured for each EB-OCT imaging site. Measurements and Main Results: Compared with the number of bronchioles in control subjects (mean = 11.2/cm3; SD = 6.2), there was significant bronchiole reduction in subjects with IPF (42% loss; mean = 6.5/cm3; SD = 3.4; P = 0.0039), including in IPF affected (48% loss; mean: 5.8/cm3; SD: 2.8; P < 0.00001) and IPF less affected (33% loss; mean: 7.5/cm3; SD: 4.1; P = 0.024) sites. Stereology metrics showed that IPF-affected small airways were significantly larger, more distorted, and more irregular than in IPF-less affected sites and control subjects. IPF less affected and control airways were statistically indistinguishable for all stereology parameters (P = 0.36-1.0). Conclusions: EB-OCT demonstrated marked bronchiolar loss in early IPF (between 30% and 50%), even in areas minimally affected by disease, compared with matched control subjects. These findings support small airway disease as a feature of early IPF, providing novel insight into pathogenesis and potential therapeutic targets.

Can FDG-PET/CT imaging be used to predict decline in quality of life in interstitial lung disease? A prospective study of the relationship between FDG uptake and quality of life in a UK outpatient setting.

BACKGROUND: 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) CT imaging has been used in many inflammatory and infectious conditions to differentiate areas of increased metabolic activity. FDG uptake differs between areas of normal lung parenchyma and interstitial lung disease (ILD). OBJECTIVES: In this study, we investigated whether FDG-PET/CT parameters were associated with a change in the quality of life (QoL) in patients with ILD over 4 years of follow-up. METHODS: Patients underwent PET-CT imaging at diagnosis and were followed up with annual QoL assessment using the St George's Respiratory Questionnaire (SGRQ) until death or 4 years of follow-up. Maximum standard uptake value (SUVmax) and Tissue-to-Background Ratio (TBR) were assessed against SGRQ overall and subscale scores. RESULTS: 193 patients (94 patients in the idiopathic pulmonary fibrosis (IPF) subgroup and 99 patients in the non-IPF subgroup) underwent baseline FDG-PET/CT imaging and QoL assessment. Weak-to-moderate correlation was observed between baseline SUVmax and SGRQ scores in both ILD subgroups. No relationship was observed between baseline SUVmax or TBR and change in SGRQ scores over 4 years of follow-up. In the IPF subgroup, surviving patients reported a decline in QoL at 4 years post diagnosis whereas an improvement in QoL was seen in surviving patients with non-IPF ILD. CONCLUSIONS: Weak-to-moderate positive correlation between baseline SUVmax and SGRQ scores was observed in both ILD subgroups (IPF:rs=0.187, p=0.047, non-IPF: rs=0.320, p=0.001). However, baseline SUVmax and TBR were not associated with change in QoL in patients with IPF and non-IPF ILD over 4 years of follow-up. At 4 years post diagnosis, surviving patients with IPF reported declining QoL whereas improvement was seen in patients with ILD who did not have IPF.

[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.