Hye Jeon Hwang

In mild idiopathic pulmonary fibrosis, quantitative CT–derived fibrosis burden and vascular remodeling were associated with acute exacerbation risk, with venous and arterial remodeling mediating fibrosis effects, supporting the value of vascular metrics for risk stratification.

In patients with combined pulmonary fibrosis and emphysema, radiologic fibrosis patterns were associated with the rate of fibrosis progression, whereas survival was primarily determined by baseline fibrosis extent and adverse clinical events, including lung cancer development.

During a 6-year follow-up, the diameter of emphysema holes at noncontrast CT varied, whereas densitometry results remained stable, and the volume of holes that increased in diameter, particularly that of new holes, was related to emphysema severity and progression.

Objectives To investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience. Materials and methods This retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated. Results Of 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; p = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; p = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others. Conclusion CBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers’ level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers. Key Points Question CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability. Findings CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall. Clinical relevance The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results. Graphical Abstract

In patients with idiopathic pulmonary fibrosis–pathologic usual interstitial pneumonia (UIP), certain autoimmune features were indeterminate for UIP or associated with alternative CT patterns and higher overall survival.

Objective. Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

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.

Objective: Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach: For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results: We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance: Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Lee HN, et al. Korean J Radiol. 2024 Jul;25(7):673-683. https://doi.org/10.3348/kjr.2024.0110