Machine learning in radiology: the new frontier in interstitial lung diseases.

Summary Rheumatoid arthritis (RA) is associated with a ten-fold increased risk of interstitial lung disease (ILD). The presence of ILD in a patient with RA influences both prognosis and the choice of therapeutic intervention. Although there has been increasing awareness of the relevance of ILD to the outcome of patients with RA, there remains limited consensus around several important aspects of disease diagnosis and management. This Review emphasises two of the main areas in which a much greater standardisation of approach is urgently needed. First, the development of a screening approach based on established risk factors is a priority. Learning how to identify patients with RA-associated ILD early and then monitoring their condition to assess progression and measure the potential influence of therapeutic interventions is essential. Second, there is a need to consolidate the therapeutic evidence base to agree a standard approach to intervention. Although we already have a range of potentially effective treatments, further clinical trials are required to optimise therapeutic intervention.

Current issues in rheumatoid arthritis-associated interstitial lung disease

This article reviews a selection of the scientific presentations on interstitial lung disease (ILD)/diffuse parenchymal lung disease (DPLD) that were made at the 2018 European Respiratory Society (ERS) International Congress in Paris. A number of advances in the epidemiology, pathogenesis, diagnosis and treatment of these disorders were presented and discussed by clinicians and researchers. The research topics span over all four groups of ERS Assembly 12: Interstitial Lung Diseases (Group 12.01: Idiopathic interstitial pneumonias; Group 12.02: ILD/DPLD of known origin; Group 12.03: Sarcoidosis and other granulomatous ILD/DPLD; Group 12.04: Rare ILD/DPLD).

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Research highlights from the 2018 ERS International Congress: interstitial lung diseases.

To investigate the prognostic value of quantitative analysis of CT among patients with idiopathic pulmonary fibrosis (IPF) by quantifying the fibrosis extent and to attempt to provide precise medium-long term prognostic predictions for individual patients.This was a retrospective cohort study that included 95 IPF patients in Zhongshan Hospital, Fudan University. 64 patients firstly diagnosed with IPF from 2009 to 2015 was included as the derivation cohort. Information regarding sex, age, the Gender-Age-Physiology (GAP) index, high-resolution computed tomography (HRCT) images, survival status, and pulmonary function parameters including forced vital capacity (FVC), FVC percent predicted (FVC%pred), diffusing capacity of carbon monoxide (DLCO), DLCO percent predicted (DLCO%pred), carbon monoxide transfer coefficient (KCO), KCO percent predicted (KCO%pred) were collected. 31 patients were included in the validation cohort. The Synapse 3D software was used to quantify the fibrotic lung volume (FLV) and total lung volume (TLV). The ratio of FLV to TLV was calculated and labeled CTFLV/TLV%, reflecting the extent of fibrosis. All the physiological variants and CTFLV/TLV% were analyzed for the dimension of survival through both univariate analysis and multivariate analysis. Formulas for predicting the probability of death based on the baseline CTFLV/TLV% were calculated by logistic regression, and validated by the validation cohort.The univariate analysis indicated that CTFLV/TLV% along with DLCO%pred, KCO%pred and GAP index were significantly correlated with survival. However, only CTFLV/TLV% was meaningful in the multivariate analysis for prognostic prediction (HR 1.114, 95% CI 1.047-1.184, P = 0.0006), and the best cutoff was 11%, based on receiver operating characteristic (ROC) curve analysis. The survival times for the CTFLV/TLV% ≤ 11% and CTFLV/TLV% > 11% groups were significantly different. Given the CTFLV/TLV% data, the death probability of a patient at 1 year, 3 years and 5 years could be calculated by using a particular formula. The formulas were tested by the validation cohort, showed high sensitivity (88.2%), specificity (92.8%) and accuracy (90.3%).Quantitative volume analysis of CT might be useful for evaluating the extent of fibrosis in the lung. The CTFLV/TLV% could be a valuable biomarker for precisely predicting the medium-long term prognosis of individual patients with IPF. 

/pdf/medium-long-term-prognosis-prediction-for-idiopathic-3syd91sq.pdf

Medium-long term prognosis prediction for idiopathic pulmonary fibrosis patients based on quantitative analysis of fibrotic lung volume

Background/Objectives: Accurate prognostication for interstitial lung disease (ILD) remains challenging, limiting clinicians’ ability to optimize patient management strategies. This study aimed to evaluate the prognostic value of baseline quantitative CT-derived indices, including total lung volume (TLVcm3), normal lung volume% (NLV%), and fibrotic lung volume% (FLV%), for predicting three-year mortality in interstitial lung disease (ILD) patients. Methods: A total of 101 ILD patients were retrospectively enrolled. Baseline CT-derived indices, including TLVcm3, NLV% (−950 to −700 HU), and FLV% (−600 to +50 HU), were measured on chest CT. Baseline forced vital capacity(FVC)% predicted and diffuse capacity of lungs for carbon monoxide (DLCO)% predicted were collected. Survival analysis used Kaplan–Meier’s curves and log-rank tests. Uni- and multivariate Cox’s proportional hazards regression were performed. Pearson’s correlation was used between CT-derived indices, FVC% predicted, and DLCO% predicted. Results: During 3-year follow-up, 30 of 101 patients (29.70%) died. Deceased patients had a significantly lower baseline NLV% (59.27% ± 7.61% vs. 65.02% ± 7.82%, p = 0.001) and a higher FLV% (17.64% ± 7.98% vs. 13.34% ± 7.48%, p = 0.011) compared with survivors. Multivariate analysis identified baseline NLV% (adjusted hazard ratio 0.88, 95% CI: 0.78–0.99, p = 0.034) and DLCO% predicted (adjusted hazard ratio 0.97, 95% CI: 0.95–0.99, p = 0.007) as independent predictors of three-year mortality. Patients with NLV% ≤ 64.15 and FLV% ≥ 14.12 showed significantly worse survival outcomes (21.78% vs. 7.92%, p &lt; 0.001;19.80% (20/101) vs. 9.90% (10/101), p &lt; 0.001). CT-derived indices moderately correlated with FVC% predicted and DLCO% predicted. Conclusions: Baseline FLV% ≥ 14.12 and NLV% ≤ 64.15 can effectively stratify and differentiate outcomes in ILD patients. Baseline NLV% has the potential as a prognostic indicator for 3-year survival in ILD. 

Interstitial Lung Disease Outcome Prediction Using Quantitative Densitometry Indices on Baseline Chest Computed Tomography

Introduction: CT is essential in diagnosis of acute exacerbation of IPF (AE) showing new ground-glass abnormalities (GGO) along to reticular pattern (RE). However, quantification is subject to inter-reader variability and limited reproducibility. Aim of this work was to compare visual scoring (VS) to fully automated computer-aided quantification of sequential CTs in patients with and without AE. Methods: Paired follow-up non-enhanced thin-section CT were analyzed using LUng FIbrosis quantification Tool (LUFIT) in 49 IPF patients (pt). Longitudinal VS assessment was performed by an experienced chest radiologist. Spearman correlation was applied for 40th and 80th percentile (PERC) histogram of lung density (reflecting RE and GGO respectively) and VS. Results: 30pt with AE (FVC 67% ±18, DLCO 35% ±13) and 19pt without AE (NoAE) (FVC 81% ±24, DLCO 49% ±16) at follow-up were included, resulting in serial analysis of 98 paired CT data-sets. Automated CT analysis ran unattended and successfully. In pt experiencing AE, GGO increased +6% in VS and +29HU (40th PERC) in LUFIT (p Conclusions: In IPF patients, LUFIT analysis identified typical changes during AE successfully and provided quantification of acute inflammation (GGO) and fibrosis (RE). Fully automated quantification of CT is easy to perform, time effective, correct, and reliable in patients suffering from IPF.

Visual and fully-automated CT analysis in acute exacerbations of idiopathic pulmonary fibrosis (IPF)

Objective Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients. Methods 418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses. Results In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92). Conclusions In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools.

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Visual and fully-automated CT analysis in acute exacerbations of idiopathic pulmonary fibrosis (IPF)

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools.