Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.

Can CT-Based Arterial and Venous Morphological Markers of Chronic Obstructive Pulmonary Disease Explain Pulmonary Vascular Remodeling?

RATIONALE AND OBJECTIVES: We analyzed changes in quantitative pulmonary artery and vein parameters to investigate pulmonary vascular remodeling characteristics in chronic obstructive pulmonary disease (COPD) patients. MATERIALS AND METHODS: This retrospective study recruited healthy volunteers and COPD patients. Participants undergoing standard-of-care pulmonary function testing (PFT) and computed tomography (CT) evaluations were classified into five groups: normal and Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4. Artery and vein analyses (volumes, numbers, densities, and fractions) were performed using artificial intelligence. RESULTS: Among 139 subjects (136 men; mean age, 64years±8 [SD]) with GOLD grade 1 (n = 13), grade 2 (n = 49), grade 3 (n = 42), grade 4 (n = 17) and control subjects (n = 18) enrolled, differences in arterial volumes (BV5-10, BV10+, pulmonary arterial volume) and venous densities (BV5 density, BV10+ density, pulmonary venous density, pulmonary venous branch density) among control and GOLD grades 1-4 were statistically significant (P < .05). Higher pulmonary arterial volumes and lower number were observed with more advanced COPD. The number and volumes of pulmonary veins were lower in GOLD grades 2 and 3 than in GOLD grade 1 but higher in GOLD grade 4 than in GOLD grade 3. The numbers and volumes of pulmonary arteries and veins showed varying positive correlations (γ = 0.18-0.96, P < .05). Pulmonary vascular densities were mildly to moderately correlated with PFT results (γ = 0.236-0.495, P < .05) and were moderately negatively correlated with the emphysema percentage (γ = -0.591 to -0.315, P < .05). CONCLUSION: Patients with COPD exhibited pulmonary vascular remodeling, which occurred in the arteries at the early grade of COPD and in the veins at the late grade. CT-based quantitative analysis of pulmonary vasculature may become an imaging marker for early diagnosis and assessment of COPD severity.

Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.

OBJECTIVES: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD). METHODS: The retrospective study included 298 CTD-ILD patients between January 2018 and May 2022. A deep learning-based RDNet model was established (1610 fully annotated CT images for training and 402 images for validation). The model was used to automatically classify and quantify 3 radiologic features (ground glass opacities [GGOs], reticulation, and honeycombing), along with a volumetric sum of 3 areas (ILD%). As a control, we used 4 previously defined CT threshold methods to calculate the ILD assessment index. The Spearman rank correlation coefficient ( r ) evaluated the correlation between various indicators and the lung function index in the remaining 184 CTD-ILD patients who were staged according to the gender-age-physiology (GAP) system. RESULTS: The RDNet model accurately identified GGOs, reticulation, and honeycombing, with corresponding Dice indexes of 0.784, 0.782, and 0.747, respectively. A total of 137 patients were at GAP1 (73.9%), 36 patients at GAP2 (19.6%), and 11 patients at GAP3 (6.0%). The percentages of reticulation and honeycombing at GAP2 and GAP3 were markedly elevated compared with those at GAP1 ( P < 0.001). The percentage of GGOs was not significantly different among the GAP stages ( P = 0.62). As the GAP stage increased, all lung function indicators tended to decrease, and the composite physiologic index (CPI) indicated an upward tendency. The percentage of honeycombs moderately correlated with the percentage of diffusing capacity of the lung for carbon monoxide (DLco%) ( r = -0.58, P < 0.001) and CPI ( r = 0.63, P < 0.001). The ILD assessment index calculated by the CT threshold method (-260 to -600 Hounsfield units) had a low correlation with DLco% and CPI (DLco%: r = -0.42, P < 0.001; CPI: r = 0.45, P < 0.001). CONCLUSIONS: The RDNet model can quantify GGOs, reticulation, and honeycombing of chest CT images in CTD-ILD patients, among which honeycombing had the most significant effect on lung function indicators. In addition, this model provided good clinical utility for evaluating the severity of CTD-ILD.

Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease.

PURPOSE: To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest. MATERIALS AND METHODS: Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results. GAP I, II, and III have 137, 36, and 11 cases, respectively. The cases in GAP Ⅱ and Ⅲ were then combined into one group, and the two groups of patients were randomly divided into the training and testing groups with a 7:3 ratio. The radiomics features were extracted using AK software. Multivariate logistic regression analysis was then conducted to establish a radiomics model. A nomogram model was established on the basis of Rad-score and clinical factors (age and gender). RESULTS: For the radiomics model, four significant radiomics features were selected to construct the model and showed excellent ability to differentiate GAP I from GAP Ⅱ and Ⅲ in both the training group (the area under the curve [AUC] = 0.803, 95% confidence interval [CI]: 0.724-0.874) and testing group (AUC = 0.801, 95% CI:0.663-0.912). The nomogram model that combined clinical factors and radiomics features improved higher accuracy of both training (88.4% vs. 82.1%) and testing (83.3% vs. 79.2%). CONCLUSION: The disease severity assessment of patients with CTD-ILD can be evaluated by applying the radiomics method based on CT images. The nomogram model demonstrates better performance for predicting the GAP staging.