Jonathan Hero Chung

Tracheal morphology and lung volume change between inspiratory and expiratory phases at chest CT can aid quality assurance and improve detection of chronic lung allograft dysfunction and decline in forced expiratory volume in 1 second in patients after lung transplantation.

Rationale: Progressive pulmonary fibrosis (PPF) is common in patients with fibrotic interstitial lung disease (ILD) and leads to high mortality. Although PPF guideline criteria include computed tomography (CT)-based progression, these measures are qualitative and prone to interreader variability. Quantitative computed tomography (qCT) measurements have the potential to overcome this limitation. Objectives: The objectives of this study were to determine whether changes in qCT measures of pulmonary fibrosis are associated with transplant-free survival (TFS) in a diverse ILD cohort and establish a quantitative computed tomography measure of progressive pulmonary fibrosis (qctPPF). Methods: A retrospective cohort analysis was performed in individuals with fibrotic ILD, including idiopathic pulmonary fibrosis (n = 350), who underwent serial chest CT for clinical indications. Commercially available software was used to generate qCT measures of pulmonary fibrosis, which were tested for association with 2-year TFS using a multivariable Cox proportional hazards model. Iterative modeling was then performed to develop a composite qctPPF measure. Results were validated in an independent ILD cohort (n = 92). Measurements and Main Results: Increasing ground-glass opacity and decreasing lung volume showed consistent association with decreased TFS across cohorts when modeled continuously and dichotomously. qctPPF classification was associated with a greater than threefold increased hazard of death or transplant in the test (hazard ratio, 4.41; 95% confidence interval, 2.77–7.03) and validation (hazard ratio, 3.54; 95% confidence interval, 1.62–7.71) cohorts. Agreement between qctPPF and radiologist-determined PPF was poor (κ = 0.20), with qctPPF classification maintaining prognostic significance when discordant with radiologist interpretation. Conclusions: Changes in qCT measures are associated with clinically relevant outcomes and could improve PPF classification.

Rationale: Incidental features of interstitial lung disease (ILD) are commonly observed on chest computed tomography (CT) scans and are independently associated with poor outcomes. Although most studies to date have relied on qualitative assessments of ILD, quantitative imaging algorithms have the potential to effectively detect ILD and assist in risk stratification for population-based cohorts. Objectives: To determine whether quantitative measures of ILD are associated with clinically relevant outcomes in the NLST (National Lung Screening Trial). Methods: Quantitative measures of ILD were generated using low-dose CT (LDCT) data collected as part of the NLST and processed with Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) and deep learning–based usual interstitial pneumonia (DL-UIP) algorithms (Imbio Inc.). A multivariable Cox proportional hazard regression model was used to test the association between ILD measures (percentage ground-glass opacity, reticular opacity, and honeycombing of total lung volume and binary DL-UIP classification) and all-cause mortality. Secondary outcomes of incident lung cancer and lung cancer mortality were also explored. Results: Quantitative CT data were generated in 11,518 individuals. Mean age was 61.5 years, and 58.7% were male. An increased risk of all-cause mortality was observed for each percentage increase in CALIPER-derived ground-glass opacity (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.01–1.02), reticular opacity (HR, 1.18; 95% CI, 1.12–1.24), and honeycombing (HR, 6.23; 95% CI, 4.23–9.16). Individuals with a positive DL-UIP classification pattern had a 4.8-fold increased risk of all-cause mortality (HR, 4.75; 95% CI, 2.50–9.04). CALIPER-derived reticular opacity was also associated with increased lung cancer–specific mortality. No quantitative measures of ILD were associated with incident lung cancer. Conclusions: Quantitative measures of ILD on LDCT are associated with clinically relevant endpoints in a large at-risk population of individuals with tobacco use history.

Background: In patients with idiopathic pulmonary fibrosis (IPF), computed tomography (CT) quantification using artificial intelligence (AI) has been explored as a method to assess the therapeutic response to antifibrotic agents; however, studies evaluating long-term follow-up outcomes remain scarce. We investigated AI-driven quantitative analysis for long-term follow-up chest CT of IPF patients undergoing pirfenidone treatment. Methods: Among the 2,223 patients diagnosed with interstitial lung disease by chest CT at Jeonbuk National University Hospital, 36 patients with a multidisciplinary diagnosis of IPF were included in the study after excluding those who had not undergone surgical lung biopsy or did not have available pulmonary function tests (PFTs). These 36 patients underwent high-resolution computed tomography (HRCT) along with concurrent PFTs over a 10-year period and were categorized into two groups: those treated with pirfenidone (n=17) and those not treated with pirfenidone (n=19). Quantitative texture analysis was performed using a deep convolutional neural network to calculate fibrotic scores, defined as the combined mean percentage of two fibrotic components—reticulation and honeycombing, with or without accompanying ground-glass opacities—across the entire lung. This analysis aimed to assess treatment response in IPF patients receiving pirfenidone. Repeated measures analysis of variance (ANOVA) was used to evaluate the correlation between changes in pulmonary function and fibrotic scores over time. Results: The final study population comprised 36 patients, with a mean age of 67.1±7.7 years. Patients (DLCO: 58.0%±21.0%) who received pirfenidone (n=17) exhibited lower DLCO values at the final follow-up compared to the untreated group (n=19) (DLCO: 69.0%±21.7%), although the difference was not statistically significant (P=0.260). However, in the treated group (n=17), patients with progression despite pirfenidone treatment (n=6) (fibrotic score: 27.1%±12.1%) showed a markedly greater increase in mean AI fibrotic scores at the final follow-up compared to those with no or little change (n=11) (fibrotic score: 10.9%±8.7%), with the difference approaching statistical significance (P=0.076). There was a significant correlation between the decrease in DLCO values and the increase in AI fibrotic score in patients with pirfenidone on long-term follow-up (PConclusions: AI-driven quantitative analysis of HRCT images in patients with IPF enables objective monitoring of the effects of pirfenidone on the progression of pulmonary fibrosis on long-term follow-up.

Given the paramount importance of CT in the diagnosis, prognosis, and monitoring of interstitial lung disease (ILD), particularly fibrotic ILD, radiologists should familiarize themselves with current imaging guidelines and emerging ILD-related concepts.

Myosteatosis markers at preoperative chest CT were associated with worse survival after resection of stage 0–IIB non–small cell lung cancer, independent of the skeletal muscle index and subcutaneous adipose tissue.

The discordance rate was low between the two current classification systems for diagnosing hypersensitivity pneumonitis on thin-section chest CT scans. Diagnostic performance of each classification... Purpose To compare the diagnostic performance of the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) versus the American College of ...

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.

Pulmonary alveolar proteinosis (PAP) is a rare disease with frequently favorable outcomes. In a minority of patients with primary or secondary PAP, the disease course may be complicated by pulmonary fibrosis (PF) despite appropriate management. Imaging and histopathologic manifestations of uncomplicated PAP are well-known. In contrast, radiologic-pathologic descriptions of PAP-associated PF (PAP-PF) are limited. The current manuscript presents three cases of PAP-PF, each with serial high-resolution CT imaging demonstrating the longitudinal progression of this unusual complication, with concordant pathologic findings in two patients. Much remains to be known regarding adverse prognostic factors contributing to PAP-PF. Early recognition of radiologic-pathologic manifestations would allow timely diagnosis and management optimization.