Evaluation Of Interstitial Lung Disease Research Articles

Novel deep-learning analysis for connective tissue disease -related interstitial lung disease extent assessment on CT: a preliminary cross-sectional study.

Physician's evaluation of interstitial lung disease (ILD) extension with high-resolution computed tomography (HRCT) has limitations such as lack of objectivity and reproducibility. This study aimed to investigate the utility of computer-based deep-learning analysis using QZIP-ILD® software (DL-QZIP) compared with conventional approaches in connective tissue disease (CTD) -related ILD. Patients with CTD-ILD visiting our Rheumatology Centre between December 2020 and April 2024 were recruited. Quantitative scores, including the percentage of lung involvement in ground-glass opacity (QGG), total fibrotic lesion (QFIB), and overall ILD extension encompassing both QGG and QFIB (QILD), calculated by DL-QZIP, were compared with semiquantitative visual method, employing intraclass correlation coefficients (ICC). We compared the capability of QILD scores to distinguish patients with forced vital capacity (FVC) % <70 in both methods determined by the area under the curve (AUC) by the receiver-operating characteristic curve analysis and DeLong's test. Eighty patients (median age, 66 years; 14 men) were included. Median QGG, QFIB, and QILD scores were 3.45%, 2.19%, and 5.35% using DL-QZIP, and 3.25%, 4.06%, and 8.48% using visual method, respectively. Correlations between DL-QZIP and visual method were 0.75 for QGG, 0.61 for QFIB, and 0.75 for QILD. The AUC of QILD scores for FVC% <70 was significantly higher with DL-QZIP (0.833) compared with visual method (0.660) (p < 0.01). QZIP-ILD® demonstrates superior capability in distinguishing patients with a radiological scenario correlated to severe physiological impairment, while showing relatively good correlations in quantifying the extent on HRCT compared with conventional method in CTD-ILD.

Artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) for the evaluation of interstitial lung disease in patients with inflammatory rheumatic diseases

High-resolution computed tomography (HRCT) is important for diagnosing interstitial lung disease (ILD) in inflammatory rheumatic disease (IRD) patients. However, visual ILD assessment via HRCT often has high inter-reader variability. Artificial intelligence (AI)-based techniques for quantitative image analysis promise more accurate diagnostic and prognostic information. This study evaluated the reliability of artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) in IRD-ILD patients and verified IRD-ILD quantification using AIqpHRCT in the clinical setting. Reproducibility of AIqpHRCT was verified for each typical HRCT pattern (ground-glass opacity [GGO], non-specific interstitial pneumonia [NSIP], usual interstitial pneumonia [UIP], granuloma). Additional, 50 HRCT datasets from 50 IRD-ILD patients using AIqpHRCT were analysed and correlated with clinical data and pulmonary lung function parameters. AIqpHRCT presented 100% agreement (coefficient of variation = 0.00%, intraclass correlation coefficient = 1.000) regarding the detection of the different HRCT pattern. Furthermore, AIqpHRCT data showed an increase of ILD from 10.7 ± 28.3% (median = 1.3%) in GGO to 18.9 ± 12.4% (median = 18.0%) in UIP pattern. The extent of fibrosis negatively correlated with FVC (ρ=-0.501), TLC (ρ=-0.622), and DLCO (ρ=-0.693) (p < 0.001). GGO measured by AIqpHRCT also significant negatively correlated with DLCO (ρ=-0.699), TLC (ρ=-0.580) and FVC (ρ=-0.423). For the first time, the study demonstrates that AIpqHRCT provides a highly reliable method for quantifying lung parenchymal changes in HRCT images of IRD-ILD patients. Further, the AIqpHRCT method revealed significant correlations between the extent of ILD and lung function parameters. This highlights the potential of AIpqHRCT in enhancing the accuracy of ILD diagnosis and prognosis in clinical settings, ultimately improving patient management and outcomes.

Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.

Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT). We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially. Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models. 1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model. Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

Radiological assessment of interstitial lung disease: what can lung ultrasound do?

The use of lung ultrasound in the screening, diagnosis, and evaluation of interstitial lung disease has been relatively well studied, but has not been widely accepted and applied in clinical practice. There are also some differences in the examination methods applied in these studies. This paper summarized the application, advantages, and disadvantages of lung ultrasound in the diagnosis and follow-up of interstitial lung disease by comprehensively reviewing the examination methods, research results and progress of new technologies of lung ultrasound in interstitial lung disease.

Diagnosis of interstitial lung diseases: from Averill A. Liebow to artificial intelligence.

Histopathologic criteria of usual interstitial pneumonia (UIP)/idiopathic pulmonary fibrosis (IPF) were defined over the years and endorsed by leading organizations decades after Dr. Averill A. Liebow first coined the term UIP in the 1960s as a distinct pathologic pattern of fibrotic interstitial lung disease. Novel technology and recent research on interstitial lung diseases with genetic component shed light on molecular pathogenesis of UIP/IPF. Two antifibrotic agents introduced in the mid-2010s opened a new era of therapeutic approaches to UIP/IPF, albeit contentious issues regarding their efficacy, side effects, and costs. Recently, the concept of progressive pulmonary fibrosis was introduced to acknowledge additional types of progressive fibrosing interstitial lung diseases with the clinical and pathologic phenotypes comparable to those of UIP/IPF. Likewise, some authors have proposed a paradigm shift by considering UIP as a stand-alone diagnostic entity to encompass other fibrosing interstitial lung diseases that manifest a relentless progression as in IPF. These trends signal a pendulum moving toward the tendency of lumping diagnoses, which poses a risk of obscuring potentially important information crucial to both clinical and research purposes. Recent advances in whole slide imaging for digital pathology and artificial intelligence technology could offer an unprecedented opportunity to enhance histopathologic evaluation of interstitial lung diseases. However, current clinical practice trends of moving away from surgical lung biopsies in interstitial lung disease patients may become a limiting factor in this endeavor as it would be difficult to build a large histopathologic database with correlative clinical data required for artificial intelligence models.

Assessment of the feasibility of 1-min sit-to-stand test in evaluating functional exercise capacity in interstitial lung disease patients.

Six-min walk test (6MWT) is widely used exercise test for the evaluation of interstitial lung disease (ILD). However, the long test duration and need for long and flat surface hinder its routine use. One-min sit-to-stand test (1-STST) is devoid of such limitations, but has been scarcely evaluated. The study was conducted to evaluate the performance of 1-STST by correlating it with 6MWT in ILD patients. Stable ILD patients were prospectively enrolled. After initial spirometry, all patients performed 6MWT and 1-STST following the standard recommendations. Exercise capacity and physiological parameters (heart rate, pulse oxygen saturation, blood pressure and dyspnea [modified Borg scale]) including peripheral oxygen saturation (SpO2) were correlated after the tests using Pearson correlation, Intraclass correlation coefficient (ICC) and kappa (κ) coefficient. The results showed that the mean age of the patients (n=60) was 58.8±11.5 years (male:female=1:1). Repetitions after 1-STST showed significant correlation with 6MWT (r=0.48; P<0.001). Changes in the physiological variables were similar (P>0.05) with good consistency (ICC=0.68-0.95) between 6MWT and 1-STST. Both lowest SpO2 and difference in SpO2 also showed good agreement (ICC=0.86; 95% confidence interval [CI], 0.77-0.92 and ICC=0.68; 95% CI, 0.47-0.81 respectively) and significant correlation (r=0.76 and r=0.52, respectively). 1-STST and 6MWT were consistent in identifying patients having oxygen desaturation ≥4% (κ=0.56; 96% CI, 0.30-0.82). The results demonstrated that the performance of 1-STST was consistent with 6MWT in terms of exercise capacity and change in physiological parameters. 1-STST can be a valid alternative to 6MWT in the assessment of ILD patients, especially in peripheral health centers.

Lung ultrasound and high-resolution computed tomography quantitative variations during nintedanib treatment for systemic sclerosis-associated interstitial lung disease.

Lung ultrasound (LUS) and high-resolution computed tomography (HRCT) are commonly used for the evaluation of interstitial lung disease (ILD). Nintedanib (NIN) is an antifibrotic therapy approved for systemic sclerosis-associated ILD (SSc-ILD). We assessed LUS and quantitative HRCT changes in SSc-ILD patients treated with NIN during a one-year follow-up, evaluating relationships between imaging variations and functional or quality-of-life outcomes. SSc-ILD patients who started NIN were enrolled and followed for twelve months. Pulmonary function tests and patient-reported outcome measures (PROMs) were assessed half-yearly and quarterly, respectively. LUS was performed quarterly evaluating the presence of B-lines (BL) and pleural line irregularities (PLI). HRCT was repeated after one year and quantitatively analysed with CALIPER software. Ten patients (70% female, mean age 62 years) were enrolled. The mean total number of both BL and PLI was constantly decreased during NIN treatment, being significantly reduced after twelve months (from 175.1 ± 66.7-120.8 ± 70.3 for BL, p= 0.005 and from 50.6 ± 32.5-37.2 ± 22.4 for PLI, p= 0.05). Male gender, smoking habit and baseline forced vital capacity <70% predicted were associated with worse LUS outcomes. A greater reduction of both BL and PLI was observed in those who improved in PROMs, especially modified Medical Research Council dyspnoea scale (p= 0.016 and p= 0.04, respectively) and Saint George's Respiratory Questionnaire (p= 0.006 and p= 0.026, respectively). No significant changes in the CALIPER percentages of normal parenchyma or ILD elements were observed after twelve months of NIN, thus paralleling the stabilization obtained at pulmonary function tests. We present preliminary results on NIN effects on SSc-ILD as assessed by LUS, a useful method for frequently repeated monitoring, and CALIPER, a valid implementation whenever a HRCT is performed.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.

Segmentation-Based Analysis of T2- and T1-Weighted Dynamic Magnetic Resonance Images Provides Adequate Observer Agreement in the Evaluation of Interstitial Lung Disease.

The aim of the study is to quantify observer agreement in the magnetic resonance imaging (MRI) classification of inflammatory or fibrotic interstitial lung disease (ILD). Our study is a preliminary analysis of a larger prospective cohort. The MRI images of 18 patients with ILD (13 females; mean age, 65 years) were acquired in a 1.5 T scanner and included axial fat-saturated T2-weighted (T2-WI, n = 18) and coronal fat-saturated T1-weighted images before and 1, 3, 5, and 10 minutes after gadolinium administration (n = 16). The MRI studies were evaluated with 2 different methods: a qualitative evaluation (visual assessment and measurement of few regions of interest; evaluations were performed independently by 5 radiologists and 3 times by 1 radiologist) and a segmentation-based analysis with software extraction of signal intensity values (evaluations were performed independently by 2 radiologists and twice by 1 radiologist). Interstitial lung disease was classified as inflammatory or fibrotic, based on previously described imaging criteria. Regarding the qualitative evaluation, intraobserver agreement was excellent (κ = 0.92, P < 0.05) for T2-WI and fair (κ = 0.29, P < 0.05) for T1 dynamic study, while interobserver agreement was moderate (κ = 0.56, P < 0.05) and poor (κ = 0.11, P = 0.18), respectively. In contrast, upon segmentation-based analysis, intraobserver and interobserver agreement were excellent for T2-WI (κ = 0.886, P < 0.001; κ = 1.00, P < 0.001; respectively); for T1-WI, intraobserver agreement was excellent (κ = 0.87, P < 0.05) and interobserver agreement was good (κ = 0.75, P < 0.05). Segmentation-based MRI analysis is more reproducible than a qualitative evaluation with visual assessment and measurement of few regions of interest.

Radiological evaluation of interstitial lung disease in patients with rheumatoid arthritis treated with abatacept or JAK inhibitors for 1 year

BackgroundWe evaluated the radiological deterioration of interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA) treated with abatacept (ABT) or Janus-kinase inhibitors (JAKi) for 1 year. MethodsThis study enrolled 84 patients with RA who had started treatment with ABT (48 patients) or JAKi (36 patients) between 2017 and 2020. All patients underwent chest CT before administration and at 1 year post-treatment. ResultsIn all patients, the rate of deterioration of ILD in ABT and JAKi was 4.2% and 5.6%, respectively (p = 0.847). In the patients with pre-existing ILD, the rates of deterioration of ILD in the ABT and JAKi groups were 10.5% and 18.2%, respectively (p = 0.611). Meanwhile, newly developed ILD did not occur in the two groups without pre-existing ILD. ConclusionThe present study demonstrated that the safety of pulmonary toxicity with ABT and JAKi treatments was similar in patients with RA. JAKi may be a potentially acceptable treatment option for patients with RA.

Comparative Analysis of Interstitial Lung Diseases on Chest Radiographs and HRCT

Introduction: Interstitial lung diseases are a diverse group of diseases which affect the lung interstium and share similar clinical and radiological manifestations. Although there are several interstitial lung diseases, only a few handful of about 10-12 account for more than 90% of them. Hence, proper knowledge and understanding of these common entities is pertinent in diagnosing them and in including them in the differential diagnosis. Aims and Objectives: The study aims to diagnose study and compare Interstitial Lung Diseases using HRCT and Chest X-Ray by using a prospective and observational study model. Results: HRCT showed statistically significant difference in diagnosis of Interstitial Lung Disease as compared to Chest X-Ray as p value was significant in 4 out of 6 assessed parameters. Hence, CT scan was found essential in evaluation of Interstitial Lung Diseases. Conclusion: High resolution computed tomography (HRCT) chest scans are essential to the diagnostic work-up since each ILD form is characterized by a specific pattern of abnormalities and a confident diagnosis can often be arrived at by HRCT alone or in correlation with the clinical symptoms.

Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis.

We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients' clinical outcomes. We prospectively included 104 patients (84 with and 20 without ILD). An expert radiologist defined regions of interest in the typical areas of normal, ground-glass opacity, consolidation, consolidation with fibrosis (traction bronchiectasis), honeycombing, reticulation, traction bronchiectasis, and emphysema, and compared them with the CAD and DL-based analysis results. Next, we measured the extent of ILD lesions with the CAD and DL-based analysis and compared them. Finally, we compared the lesion extent on computed tomography (CT) images, as measured with the DL-based analysis, with pulmonary function tests results and patients' overall survival. Pearson's correlation analysis revealed a significant correlation between DL-based analysis and CAD results. Forced vital capacity was significantly correlated with DL-based analysis (r = 0.789, p &lt; 0.001 for normal lung volume and r = -0.316, p = 0.001 for consolidation with fibrosis volume). Consolidation with fibrosis measured using DL-based analysis was independently associated with poor survival. The lesion extent measured using DL-based analysis showed a negative correlation with the pulmonary function test results and prognosis.

Evaluation of Interstitial Lung Disease in Idiopathic Inflammatory Myopathies Through Semiquantitative and Quantitative Analysis of Lung Computed Tomography.

To perform a semiquantitative and quantitative analysis of interstitial lung disease (ILD), through computed tomography (CT), in different serological subgroups of idiopathic inflammatory myopathies (IIM) patients, to find radiologic and clinical differences of disease related to serology. This was a prospective study, which included 98 IIM patients, divided into serological subgroups: anti-aminoacyl-transfer-RNA-synthetases (anti-ARS) positive and myositis-specific autoantibodies (MSA) negative.For each baseline CT the total semiquantitative score of Warrick (WS) and the automated software (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) quantitative scores interstitial lung disease % (ILD%) and vascular-related structure % (VRS%) were calculated. Pulmonary function tests included total lung capacity % (TLC%), forced vital capacity % (FVC%), and diffusing capacity of the lung for carbon monoxide % (DLCO%). Inverse correlations ( P <0.001) between the radiologic scores and the functional scores DLCO% and TLC% were found, the most relevant being between ILD% and DLCO% (ρ=-0.590), VRS% and DLCO% (ρ=-0.549), and WS and DLCO% (ρ=-0.471).Positive correlations between ILD% and VRS% (ρ=0.916; P <0.001), WS and ILD% (ρ=0.663; ρ<0.001), and WS and VRS% (ρ=0.637; P <0.001) were obtained.Statistically significant higher values of WS, ILD%, and VRS% were found in the anti-ARS group (WS=15; ILD%=11; VRS%=3.5) compared with the MSA negative one (WS=2.5; ILD%=0.84; VRS%=2.2).The nonspecific interstitial pneumonia pattern was dominant. No statistically significant differences emerged at pulmonary function tests. In this study, ILD in anti-ARS-positive and MSA-negative groups was defined through semiquantitative and quantitative analysis of lung CT. The inverse correlations between the radiologic scores and TLC% and DLCO% ( P <0.001) confirm the role of lung CT in the evaluation of ILD in IIM.

HIGH RESOLUTION CT IMAGING IN THE EVUALTION OF INTERSTITIAL LUNG DISEASES AND IT'S COMPARISION WITH CHEST X-RAY

Introduction: Interstitial lung disease( ILD) is one of the evolving disease that's needs to be diagnosed by various modalitites and treated in today's modern era. Some of the diseases are associated with signicant morbidity and mortality. Thus, we aimed to determine the radiological ndings of various Interstitial lung disease in HRCT Thorax and compare it with Chest x-ray to get a idea on how much chest x-ray and HRCT Thorax is effective in diagnosis of Interstitial lung disease for better outcome of disease. A cross sectional study was conducted on 120Method: patients with strong clinical suspicion of interstitial lung disease who were referred to the department of Radiodiagnosis, S.N Medical College Agra after taking detailed clinical history 120 patients underwent Chest X-ray and MDCT (64 Slice) examination of the thorax for evaluation of interstitial lung disease( ILD). Result: The main observation in our study was that higher number of patients with ndings were detected by HRCT thorax in comparision to Chest -Xray. We were able to detect higher no. of patients with ndings of septal thickenings, nodular opacities in HRCT Thorax in comparision to X-ray Chest and this difference between the two methods was found to be statistically signicant (P&lt;0.05). We were able to detect higher no. of patients with ndings of reticular opacity, ground glass opacity, Traction bronchiectasis, honeycombing in HRCT Thorax in comparision to X-ray Chest and the difference between the two methods was not statistically signicant (P&gt;0.05). The conclusion of this study is that HRCTConclusion: Thorax is much more sensitive than conventional chest radiography in the assessment and diagnosis of patients with interstitial lung diseases.

Ultrasound evaluation of interstitial lung disease in rheumatoid arthritis and autoimmune diseases.

The interpretation of lung ultrasound (US) is the result of the analysis of artifacts, rather than exact representations of anatomical structures, which appear when changes in the physical properties of the lung occur. Its application to the study of interstitial lung disease (ILD) associated with autoimmune diseases has aroused great interest in the last 10 years, as evidenced by a growing number of publications studying its usefulness in the diagnostic process, as a prognostic marker, and as an aid in monitoring of patients. The main elements in lung US interpretation in ILD are the B lines and the changes in the pleural line. B lines are vertical artifacts that are generated when there is a partial decrease in the air content of the lung parenchyma and/or the volume of the interstitial area expands. Pleural line alterations that can be seen are irregularities, thickening, fragmentation, or subpleural nodules. Both the B lines and the changes in the pleural line have shown a significant positive correlation with the evidence on chest computed tomography (high-resolution computed tomography [HRCT]) of ILD associated with autoimmune diseases, with sensitivity and negative predictive values of up to 100%. These results, together with the safety, accessibility, and low cost of lung US, support this imaging technique as a promising screening method for optimizing the indication for HRCT. The role of lung US regarding sensitivity to change needs further investigation with multicenter prospective studies.

Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis.

IntroductionComputer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS).Materials and methodsWe performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care.ResultsThere was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001).ConclusionOur study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality.Key Points• The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients.• VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001.• Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality.

Role of HRCT in evaluation of Interstitial Lung Disease

Abstract: In this study, HRCT is the examination technique of choice as it is quickly accomplished, readily available and does not require ancillary studies using other imaging technologies in most cases. In this given study, A total of 40 patients were included in which 16 patients were found to be having ILDs , there were 10 male patients (62.5%) and 6 were female patients i.e., (37.5%) of total number of patients .Idiopathic pulmonary fibrosis were found in 3 (18.75%) patients, Usual interstitial pneumonia in 2 (12.5%) patients, Chronic Hypersensitive pneumonitis in 1 (6.25%) patient , Fibrotic hypersensitive pneumonitis in 4 (25%) patients and Pulmonary interstitial edema in 6 (37.5%). The maximum patients were found in pulmonary interstitial edema and minimum patients in Chronic Hypersensitive pneumonitis, the highest number of patients with interstitial lung disease were from the 60-80 Years age group category, which was 8 Out of 16 i.e. 50% of the total number of the patients and no patients were found from the age group 0-18 years age group and 80-100 years age group.

What are the factors affecting the recovery rate of bronchoalveolar lavage fluid?

BackgroundBronchoalveolar lavage (BAL) is a useful examination for the evaluation of interstitial lung disease. A high BAL fluid (BALF) recovery rate is desirable because low recovery rates lead to inaccurate diagnoses and increased adverse events. Few studies have explored whether BALF recovery rates are influenced by clinical factors.ObjectivesThis study aimed to identify the clinical parameters affecting the recovery rates of BALF and the extent of their effects.MethodData from patients who underwent BAL at the Chiba University Hospital between 2013 and 2019 were retrospectively reviewed. BAL was performed with three aliquots of 50‐ml physiological saline. The potential association of the BALF recovery rate with clinical parameters such as age, sex, smoking status, underlying disease, bronchus used for the procedure and pulmonary function, was analysed.ResultsEight hundred twenty‐six patients had undergone BAL. The average recovery rate was 52.4%. Factors affecting BALF recovery rates included male sex (odds ratio [OR]: 0.32, 95% confidence interval [CI]: 0.20–0.53, p < 0.001); age ≥ 65 years (OR: 0.50, 95% CI: 0.33–0.76, p < 0.001); use of the left bronchus (OR: 0.46, 95% CI: 0.30–0.71, p = 0.001) and bronchi other than the middle lobe bronchus or lingula (OR: 0.41, 95% CI: 0.25–0.65, p < 0.001); and forced expiratory volume in 1 s divided by forced vital capacity <80% (OR: 0.42, 95% CI: 0.40–1.00, p < 0.001).ConclusionSex, age, bronchus used for the procedure and pulmonary function may be useful as pre‐procedural predictors of BALF recovery rates.

Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

High-Resolution Computed Tomography Evaluation of Interstitial Lung Disease for the Pulmonologist

Interstitial lung disease encompasses variety of entities that have diverse clinical manifestations, prognosis, and treatment options. It is imperative to identify the specific disease early as delays in diagnosis can lead to irreversible damage. Studies demonstrate that treatment with certain medications and avoidance of exposures that potentiate injury can reduce disease progression. The use of high-resolution computed tomography has become essential for the evaluation of interstitial lung disease. Being able to accurately identify the characteristic patterns of abnormalities can lead to expedited diagnosis and has obviated the need of other diagnostic studies such as lung biopsy. High-resolution computed tomography imaging of the chest has changed the approach to the diagnosis and treatment of interstitial lung diseases. Given the numerous entities that compose this group and options for individualized treatment plans, it is imperative for pulmonologists to recognize the defining imaging characteristics.