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.

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.

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.

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.

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.

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.

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.

[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.

[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.

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.

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.

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.

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.

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.

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.

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.