Interstitial lung abnormalities are associated with decreased mean telomere length

Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the Boston Lung Cancer Study

Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated ILA probability prediction models using machine learning techniques on CT images. Materials and Methods This secondary analysis of a retrospective study included CT scans from patients in the Boston Lung Cancer Study collected between February 2004 and June 2017. Visual assessment of ILAs by two radiologists and a pulmonologist served as the ground truth. Automated ILA probability prediction models were developed that used a stepwise approach involving section inference and case inference models. The section inference model produced an ILA probability for each CT section, and the case inference model integrated these probabilities to generate the case-level ILA probability. For indeterminate sections and cases, both two- and three-label methods were evaluated. For the case inference model, we tested three machine learning classifiers (support vector machine [SVM], random forest [RF], and convolutional neural network [CNN]). Receiver operating characteristic analysis was performed to calculate the area under the receiver operating characteristic curve (AUC). Results A total of 1382 CT scans (mean patient age, 67 years ± 11 [SD]; 759 women) were included. Of the 1382 CT scans, 104 (8%) were assessed as having ILA, 492 (36%) as indeterminate for ILA, and 786 (57%) as without ILA according to ground-truth labeling. The cohort was divided into a training set (n = 96; ILA, n = 48), a validation set (n = 24; ILA, n = 12), and a test set (n = 1262; ILA, n = 44). Among the models evaluated (two- and three-label section inference models; two- and three-label SVM, RF, and CNN case inference models), the model using the three-label method in the section inference model and the two-label method and RF in the case inference model achieved the highest AUC, at 0.87. Conclusion The model demonstrated substantial performance in estimating ILA probability, indicating its potential utility in clinical settings. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zagurovskaya in this issue.

Interstitial Lung Abnormalities: Current Understanding

Interstitial lung abnormalities (ILAs) are incidental findings on computed tomography scans, characterized by nondependent abnormalities affecting more than 5% of any lung zone. They are associated with factors such as age, smoking, genetic variants, worsened clinical outcomes, and increased mortality. Risk stratification based on clinical and radiological features of ILAs is crucial in clinical practice, particularly for identifying cases at high risk of progression to pulmonary fibrosis. Traction bronchiectasis/bronchiolectasis index has emerged as a promising imaging biomarker for prognostic risk stratification in ILAs. These findings suggest a spectrum of fibrosing interstitial lung diseases, encompassing from ILAs to pulmonary fibrosis.

Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report

Despite progress in elucidation of disease mechanisms, identification of risk factors, biomarker discovery, and the approval of two medications to slow lung function decline in idiopathic pulmonary fibrosis and one medication to slow lung function decline in progressive pulmonary fibrosis, pulmonary fibrosis remains a disease with a high morbidity and mortality. In recognition of the need to catalyze ongoing advances and collaboration in the field of pulmonary fibrosis, the NHLBI, the Three Lakes Foundation, and the Pulmonary Fibrosis Foundation hosted the Pulmonary Fibrosis Stakeholder Summit on November 8-9, 2022. This workshop was held virtually and was organized into three topic areas: 1) novel models and research tools to better study pulmonary fibrosis and uncover new therapies, 2) early disease risk factors and methods to improve diagnosis, and 3) innovative approaches toward clinical trial design for pulmonary fibrosis. In this workshop report, we summarize the content of the presentations and discussions, enumerating research opportunities for advancing our understanding of the pathogenesis, treatment, and outcomes of pulmonary fibrosis.

Lung transplantation for emphysema: only emphysema or something else?

Background

Some studies have reviewed lung explants histology to determine the frequency of pretransplant non-identified neoplasms or explore its diagnostic correlation with a previous diagnosis of interstitial lung disease (ILD). This study aims to review the histopathology of explants from patients who underwent lung transplantation (LT).

Methods

A retrospective, single-center study that included patients who underwent LT for emphysema between 01 January 2011 and 31 October 2021. The control group was composed of patients with lung cancer who underwent a lung resection between 01 November 2011 and 31 December 2019 and had a previous diagnosis of chronic obstructive pulmonary disease (COPD) prior to lung resection surgery. A systematic review was performed of histological findings to compare the frequency of additional histological diagnoses.

Results

The study sample included 160 patients (43.8%) who received a lung transplant for emphysema and 205 patients with COPD and lung cancer treated surgically. Although the patients in the cancer group were significantly older and had more comorbidities and higher cumulative tobacco consumption, transplant recipients received an additional significative histologic diagnosis more frequently (58.1% vs. 12.7%; P<0.001) including ILD, pneumoconiosis and others.

Conclusions

Significant additional histological findings were more frequent in the group of lung transplant recipients with emphysema. Notably, these findings were not explained by tobacco use, and they were significantly more frequent in transplant recipients than in patients with a previous diagnosis of COPD and higher cumulative tobacco consumption but with a better respiratory functional status.

Precision medicine advances in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a highly heterogeneous, unpredictable and ultimately lethal chronic lung disease. Over the last decade, two anti-fibrotic agents have been shown to slow disease progression, however, both drugs are administered uniformly with minimal consideration of disease severity and inter-individual molecular, genetic, and genomic differences. Advances in biological understanding of disease endotyping and the emergence of precision medicine have shown that "a one-size-fits-all approach" to the management of chronic lung diseases is no longer appropriate. While precision medicine approaches have revolutionized the management of other diseases such as lung cancer and asthma, the implementation of precision medicine in IPF clinical practice remains an unmet need despite several reports demonstrating a large number of diagnostic, prognostic and theragnostic biomarker candidates in IPF. This review article aims to summarize our current knowledge of precision medicine in IPF and highlight barriers to translate these research findings into clinical practice.

Contemporary Concise Review 2022: Interstitial lung disease

Novel genetic associations for idiopathic pulmonary fibrosis (IPF) risk have been identified. Common genetic variants associated with IPF are also associated with chronic hypersensitivity pneumonitis. The characterization of underlying mechanisms, such as pathways involved in myofibroblast differentiation, may reveal targets for future treatments. Newly identified circulating biomarkers are associated with disease progression and mortality. Deep learning and machine learning may increase accuracy in the interpretation of CT scans. Novel treatments have shown benefit in phase 2 clinical trials. Hospitalization with COVID-19 is associated with residual lung abnormalities in a substantial number of patients. Inequalities exist in delivering and accessing interstitial lung disease specialist care.

MUC5B, telomere length and longitudinal quantitative interstitial lung changes: the MESA Lung Study

BACKGROUND

The MUC5B promoter variant (rs35705950) and telomere length are linked to pulmonary fibrosis and CT-based qualitative assessments of interstitial abnormalities, but their associations with longitudinal quantitative changes of the lung interstitium among community-dwelling adults are unknown.

METHODS

We used data from participants in the Multi-Ethnic Study of Atherosclerosis with high-attenuation areas (HAAs, Examinations 1-6 (2000-2018)) and MUC5B genotype (n=4552) and telomere length (n=4488) assessments. HAA was defined as the per cent of imaged lung with attenuation of -600 to -250 Hounsfield units. We used linear mixed-effects models to examine associations of MUC5B risk allele (T) and telomere length with longitudinal changes in HAAs. Joint models were used to examine associations of longitudinal changes in HAAs with death and interstitial lung disease (ILD).

RESULTS

The MUC5B risk allele (T) was associated with an absolute change in HAAs of 2.60% (95% CI 0.36% to 4.86%) per 10 years overall. This association was stronger among those with a telomere length below an age-adjusted percentile of 5% (p value for interaction=0.008). A 1% increase in HAAs per year was associated with 7% increase in mortality risk (rate ratio (RR)=1.07, 95% CI 1.02 to 1.12) for overall death and 34% increase in ILD (RR=1.34, 95% CI 1.20 to 1.50). Longer baseline telomere length was cross-sectionally associated with less HAAs from baseline scans, but not with longitudinal changes in HAAs.

CONCLUSIONS

Longitudinal increases in HAAs were associated with the MUC5B risk allele and a higher risk of death and ILD.

Interstitial Lung Abnormalities at CT: Subtypes, Clinical Significance, and Associations with Lung Cancer

Interstitial lung abnormality (ILA) is defined as an interstitial change detected incidentally on CT images. It is seen in 4%-9% of smokers and 2%-7% of nonsmokers. ILA has a tendency to progress with time and is associated with respiratory symptoms, decreased exercise capability, reduced pulmonary function, and increased mortality. ILAs can be classified into three subcategories: nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic. In cases of ILA, clinically significant interstitial lung disease should be identified and requires clinically driven management by a pulmonologist. Risk factors for the progression of ILA include clinical elements (ie, inhalation exposures, medication use, radiation therapy, thoracic surgery, physiologic findings, and gas exchange findings) and radiologic elements (ie, basal and peripheral predominance and fibrotic findings). It is recommended that individuals with one or more clinical or radiologic risk factors for progression of ILA be actively monitored with pulmonary function testing and CT. To avoid overcalling ILA at CT, radiologists must recognize the imaging pitfalls, including centrilobular nodularity, dependent abnormality, suboptimal inspiration, osteophyte-related lesions, apical cap and pleuroparenchymal fibroelastosis-like lesions, aspiration, and infection. There is a close association between ILA and lung cancer, and many studies have reported an increased incidence of lung cancer, worse prognoses, and/or increased pulmonary complications in relation to cancer treatment in patients with ILA. ILA is considered to be an important comorbidity in patients with lung cancer. Accordingly, all radiologists involved with body CT must have sound knowledge of ILAs owing to the high prevalence and potential clinical significance of these anomalies. An overview of ILAs, including a literature review of the associations between ILAs and lung cancer, is presented. ©RSNA, 2022.