Extraction of the subpleural lung region from computed tomography images to detect interstitial lung disease

2017 ◽  
Vol 35 (11) ◽  
pp. 681-688 ◽  
Author(s):  
Tae Iwasawa ◽  
Yuma Iwao ◽  
Tamiko Takemura ◽  
Koji Okudela ◽  
Toshiyuki Gotoh ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Lung Region ◽  
Computed Tomography Images

scholarly journals Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

2021 ◽  
Vol 31 (4) ◽  
pp. 730-738
Author(s):  
Sunita Agarwala ◽  
Abhishek Kumar ◽  
Ashis Kumar Dhara ◽  
Sumitra Basu Thakur ◽  
Anup Sadhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Convolutional Neural Network ◽  
Lung Disease ◽  
Disease Patterns ◽  
Computed Tomography Images

scholarly journals Computer-Aided Quantification of Interstitial Lung Disease from High Resolution Computed Tomography Images in Systemic Sclerosis: Correlation with Visual Reader-Based Score and Physiologic Tests

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fausto Salaffi ◽  
Marina Carotti ◽  
Silvia Bosello ◽  
Alessandro Ciapetti ◽  
Marwin Gutierrez ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Systemic Sclerosis ◽  
High Resolution ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Pulmonary Function Tests ◽  
Linear Regression Analysis ◽  
Receiver Operating Curve ◽  
High Resolution Computed Tomography ◽  
Computed Tomography Images

Objective.To evaluate the performance of a computerized-aided method (CaM) for quantification of interstitial lung disease (ILD) in patients with systemic sclerosis and to determine its correlation with the conventional visual reader-based score (CoVR) and the pulmonary function tests (PFTs).Methods.Seventy-nine patients were enrolled. All patients underwent chest high resolution computed tomography (HRCT) scored by two radiologists adopting the CoVR. All HRCT images were then analysed by a CaM using a DICOM software. The relationships among the lung segmentation analysis, the readers, and the PFTs results were calculated using linear regression analysis and Pearson’s correlation. Receiver operating curve analysis was performed for determination of CaM extent threshold.Results.A strong correlation between CaM and CoVR was observed (P<0.0001). The CaM showed a significant negative correlation with forced vital capacity (FVC) (P<0.0001) and the single breath carbon monoxide diffusing capacity of the lung (DLco) (P<0.0001). A CaM optimal extent threshold of 20% represented the best compromise between sensitivity (75.6%) and specificity (97.4%).Conclusions.CaM quantification of SSc-ILD can be useful in the assessment of extent of lung disease and may provide reliable tool in daily clinical practice and clinical trials.

scholarly journals AB0605 CLINICAL PROFILE AND CHEST HIGH-RESOLUTION COMPUTED TOMOGRAPHY (HRCT) FINDINGS IN PATIENTS WITH CONNECTIVE TISSUE DISEASES AND INTERSTITIAL LUNG DISEASE: EXPERIENCE OF A SINGLE REFERENCE RHEUMATOLOGY CENTER

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1598.2-1599
Author(s):  
I. Rusu ◽  
L. Muntean ◽  
M. M. Tamas ◽  
I. Felea ◽  
L. Damian ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Systemic Sclerosis ◽  
High Resolution ◽  
Interstitial Lung Disease ◽  
Connective Tissue ◽  
Lung Disease ◽  
Interstitial Pneumonia ◽  
Connective Tissue Diseases ◽  
High Resolution Computed Tomography ◽  
Hrct Patterns

Background:Interstitial lung disease (ILD) is a common manifestation of connective tissue diseases (CTDs), and is associated with significant morbidity and mortality. Chest high-resolution computed tomography (HRCT) play an important role in the diagnosis of ILD and may provide prognostic information.Objectives:We aimed to characterize the clinical profile and chest HRCT abnormalities and patterns of patients diagnosed with CTDs and ILD.Methods:In this retrospective, observational study we included 80 consecutive patients with CTDs and ILD referred to a tertiary rheumatology center between 2015 and 2019. From hospital charts we collected clinical data, immunologic profile, chest HRCT findings. HRCT patterns were defined according to new international recommendations.Results:Out of 80 patients, 64 (80%) were women, with a mean age of 55 years old. The most common CTD associated with ILD was systemic sclerosis (38.8%), followed by polymyositis (22.5%) and rheumatoid arthritis (18.8%). The majority of patients had dyspnea on exertion (71.3%), bibasilar inspiratory crackles were present in 56.3% patients and 10% had clubbing fingers. Antinuclear antibodies (ANA) were present in 78.8% patients, and the most frequently detected autoantibodies against extractable nuclear antigen were anti-Scl 70 (28.8%), followed by anti-SSA (anti-Ro, 17.5%), anti-Ro52 (11.3%) and anti-Jo (7.5%). Intravenous cyclophosphamide therapy for 6-12 months was used in 35% of patients, while 5% of patients were treated with mycophenolate mofetil.The most frequent HRCT abnormalities were reticular abnormalities and ground glass opacity. Non-specific interstitial pneumonia (NSIP) was identified in 46.3% CTDs patients. A pattern suggestive of usual interstitial pneumonia (UIP) was present in 32.5% patients, mainly in patients with systemic sclerosis. In 21.3% patients the HRCT showed reticulo-nodular pattern, micronodules and other abnormalities, not diagnostic for UIP or NSIP pattern.Conclusion:Nonspecific interstitial pneumonia (NSIP) is the most common HRCT pattern associated with CTDs. Further prospective longitudinal studies are needed in order to determine the clinical and prognostic significance of various HRCT patterns encountered in CTD-associated ILD and for better patient management.References:[1]Ohno Y, Koyama H, Yoshikaua T, Seki S. State-of-the-Art Imaging of the Lung for Connective Tissue Disease (CTD). Curr Rheumatol Rep. 2015;17(12):69.[2]Walsh SLF, Devaraj A, Enghelmeyer JI, Kishi K, Silva RS, Patel N, et al. Role of imaging in progressive-fibrosing interstitial lung diseases. Eur Respir Rev. 2018;27(150)Disclosure of Interests:None declared

scholarly journals A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients

2021 ◽  
Vol 82 ◽  
pp. 28-39
Author(s):  
L. Berta ◽  
C. De Mattia ◽  
F. Rizzetto ◽  
S. Carrazza ◽  
P.E. Colombo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Disease ◽  
Automatic Analysis ◽  
Patient Specific ◽  
Specific Approach ◽  
Computed Tomography Images

scholarly journals AB0235 RITUXIMAB (BIOSIMILAR) IN RHEUMATOID ARTHRITIS: CLINICAL & IMMUNOLOGICAL RESPONSE TO VARYING DOSES- EXPERIENCES FROM A TERTIARY CARE CENTER IN SOUTH INDIA

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1143.2-1144
Author(s):  
J. Antony ◽  
R. Sankaralingam ◽  
R. Maheshwari ◽  
B. Chilukuri ◽  
S. Chinnadurai
Keyword(s):  
Rheumatoid Arthritis ◽  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Complete Remission ◽  
Lung Disease ◽  
Clinical Remission ◽  
Clinical Indication ◽  
Fixed Dose ◽  
Double Negative ◽  
Lung Involvement

Background:Rituximab (RTX) is a chimeric monoclonal antibody against CD20. There is a paucity of studies done with RTX biosimilars. This is a Retrospective and Observational study from January 2018 to December 2019 done in the Department of Clinical Immunology & Rheumatology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India.Objectives:1.To find the effects of varying doses of RTX in attaining clinical remission in RA.2.To find if CD19, CD20 & IgG help in identifying impending flare & if these levels help in deciding the timing of the next dose of RTX.Methods:Rheumatoid arthritis (RA) cases who were given Rituximab from January 2018 were selected. Clinical Response at 6 & 12 months & wherever feasible at 18 & 24 months was assessed by Simplified Disease Activity index (SDAI). RTX initial dose was given at 0 and 14 days followed by fixed dose at six months interval.CD19, CD20 B cell count, IgG levels were tested in patients in whom it was feasible at baseline & 6 months (select patients at 12,18 &24 months). Patients were divided in to 5 groups (DMARD naïve, DMARD resistant & Interstitial Lung disease (ILD) [Lung involvement>20% in Computed Tomography (CT)]) and (500mg & 1g). Patients were divided into three clinical groups, (DMARD naïve, DMARD resistant & Interstitial Lung disease (ILD) [Lung involvement>20% in Computed Tomography (CT)]) and two treatment groups (500mg & 1g) based on clinical indication for RTX and dose of RTX, respectively. In patients with ILD, CT scan & FVC were compared at baseline & 12 months.Results:29 patients (seropositive 28 (RF/Anti CCP/BOTH+VE), seronegative 1) were given RTX for RA over a 2-year period of which 12 had CD19, CD20 & IgG tested. Mean SDAI reduction from baseline to 6 months post treatment was 30%, 32% & 14% while complete remission (SDAI<3.3) was attained in 100%, 18% & 20% in DMARD naïve, DMARD resistant & ILD groups, respectively. CD19, CD20 & IgG reduced from 18.6%, 18.4% & 18.53g/L to 3.7%,3.7% & 9.7g/L respectively FVC improved from 62.4% to 67% at 12. The percentage of patients with lung involvement >20% reduced from 53.3% to 46.7%. Flare was observed in one patient who received 500mg RTX. CD19, CD20 & IgG levels increased from 7.9%, 8% & 9.8g/L to 27%, 25% & 13g/L respectively. 3 patients in the 1g group were followed up at 12,18 & 24 months. In these patients there were no flares or worsening symptoms. 1 patient was double negative for RF & Anti CCP and this patient did not attain clinical remission even after 2 doses of 1g RTX.Conclusion:[1]Patients with early arthritis (diagnosis made within 1 year) and who were DMARD naïve had an excellent response to Rituximab.[2]Complete remission was observed in more patients the 1g compared to 500mg group.[3]Reduction in CD19 & CD20 was associated with significant reduction in the SDAI score.[4]There was no significant reduction of CD19 & CD20 with 500mg dose of Rituximab where either a partial remission or mild flare was observed.[5]There was reduction in the lung involvement to less than 20%(CT) in few patients with 1g dose.[6]Double negative Rheumatoid arthritis poorly responded to Rituximab.[7]The positive effects of 1g Rituximab could be noted up to 24 months.[8]Flare of RA was associated with significant increase in CD19 & CD20.Disclosure of Interests:None declared

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals POS0866 TWO-DIMENSIONAL HRCT-BASED RADIOMIC FEATURES IN SSC-ILD DISTINGUISH DRUG RESPONDERS FROM NON-RESPONDERS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 688-689
Author(s):  
C. Meier ◽  
M. Maciukiewicz ◽  
M. Brunner ◽  
J. Schniering ◽  
H. Gabrys ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Linear Regression ◽  
Lung Disease ◽  
Treatment Response ◽  
Two Dimensional ◽  
Research Support ◽  
Pre Treatment

Background:Management of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) is complicated by high inter-patient variability. To date, no validated predictors of treatment response are available for routine use. High resolution computed tomography (HRCT)-based radiomics, i.e. the high-dimensional, quantitative analysis of imaging metadata, have previously been shown to be successful in discriminating (SSc-)ILD phenotypes in preclinical and clinical studies1. Since HRCT is an integral part of the routine work-up in SSc, HRCT-based radiomic features may hold potential as non-invasive biomarkers.Objectives:To predict treatment response using two-dimensional (2D) HRCT-based radiomics in SSc-ILD patients from a prospectively followed cohort.Methods:Inclusion criteria were diagnosis of SSc-ILD in HRCT, availability of a suitable chest HRCT scan within 12 months prior to initiation of a new treatment, and availability of clinical baseline and follow-up information. Treatment response was defined as the absence of all of the following over a follow-up period of 12-24 months: relative decrease in forced vital capacity (FVC) ≥5%, increase of ILD in HRCT as assessed by a radiologist, change in treatment regimen due to insufficient response, ILD-related death or lung transplantation. Of each pre-treatment HRCT, 6 slices (15±5 mm apart, starting from the basal lung margin) were manually segmented and 1513 2D radiomic features were extracted using the in-house software Z-Rad (Python 2.7). Features were Z-score transformed and pre-filtered for inter- and intra-reader robustness (intraclass correlation coefficient >0.85) and inter-feature correlation (Spearman’s rho <0.9). A categorical linear regression model was created using 3-fold cross-validated elastic nets for feature selection. Features were then summarized and divided by their number. For generation of a score cut-off, Youden’s score was used. For two-group analyses of continuous variables, Wilcoxon’s test was performed, whereas categorical data was assessed using Fisher’s exact test.Results:A total of 64 pre-treatment HRCTs from 54 patients were analyzed. In 9 patients, >1 asynchronous treatments were assessed, while 45 patients had only 1 eligible treatment approach. The response rate within the assessed follow-up period was 45.3% (n=29). For score generation, 13 radiomic features were selected and an optimal cut-off value of -0.1589 was determined. Univariate linear regression showed significant association between our categorical radiomics-based score and treatment response (p=0.007, area under the curve = 0.65 (0.51-0.79), sensitivity=0.90, specificity=0.43), whereby a high score was predictive for treatment response.No differences between patients with high (n=46) or low (n=18) scores were detected for baseline age (mean±SD=55.5±12.0 and 55.5±13.6 years, p=0.84), duration of SSc (mean±SD=6.2±8.4 and 4.7±4.4 years, p=0.79), time since ILD diagnosis (2.7±2.9 and 2.4±3.1 years, p=0.59), FVC (77.6±20.6 and 80.1±17.9, p=0.41) or DLco (54.4±21.0 and 57.6±18.9, p=0.40). Distribution of anti-Scl-70 positivity (45.7% vs. 55.6%, p=0.58) and diffuse cutaneous disease (47.7% vs. 61.1%, p=0.41) was not significantly different between patients with high and low scores, respectively, although a trend towards higher percentages in the high score group was observed.Conclusion:Our results indicate that, following validation in external cohorts, radiomics may be a promising tool for future pre-treatment patient stratification. Moreover, our radiomics-based score seems not to be associated with commonly studied clinical predictors such as anti-Scl-70 positivity or lung function, underlining a possible additive value to ‘traditional’ clinical parameters.References:[1]Schniering, J., et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. medRxiv [Preprint] doi:10.1101/2020.06.09.20124800 (2020).Disclosure of Interests:Chantal Meier: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Janine Schniering: None declared, Hubert Gabrys: None declared, Anja Kühnis: None declared, Oliver Distler Speakers bureau: Speaker fee on Scleroderma and related complications: Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche. Speaker fee on rheumatology topic other than Scleroderma: MSD, iQone, Novartis, Pfizer, Roche, Consultant of: Consultancy fee for Scleroderma and its complications: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Bayer, Baecon Discovery, Boehringer, CSL Behring, ChemomAb, Corbus Pharmaceuticals, Horizon Pharmaceuticals, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, Kymera, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Roivant Sciences, Sanofi, UCB. Consultancy fee for rheumatology topic other than Scleroderma: Abbvie, Amgen, Lilly, Pfizer, Grant/research support from: Research Grants to investigate the pathophysiology and potential treatment of Scleroderma and its complications: Kymera Therapeutics, Mitsubishi Tanabe, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Speaker fees from Boehringer-Ingelheim, Grant/research support from: Grant/research support from AbbVie, Protagen, Novartis Biomedical Research, congress support from Pfizer, Roche, Actelion, mepha, and MSD

Feasibility, face, and content validity of quantitative computed tomography in interstitial lung disease related to connective tissue diseases

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gianluca Sambataro ◽  
Domenico Sambataro ◽  
Martina Orlandi ◽  
Sofia Battisti ◽  
Lorenzo Cavagna ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Connective Tissue ◽  
Lung Disease ◽  
Content Validity ◽  
Online Survey ◽  
Objective Assessment ◽  
Quantitative Computed Tomography ◽  
Connective Tissue Diseases ◽  
Face Validity

Abstract Objectives Quantitative computed tomography (QCT) is a promising tool for objective assessment of interstitial lung disease (ILD) related to connective tissue diseases (CTD). However, its validity was never investigated. The aim of this study was to assess QCT feasibility, face, and content validity evaluation concerning CTD-ILD. Methods A rheumatologist and a chest radiologist conceived an online survey with nine statements: Two about general issue involving CTD-ILD, one for the face validity, three both for content validity and feasibility. Each statement had to be rated with a score from 0 to 100, respectively, (complete disagreement and agreement). We considered a statement supported by the experts if the median score was ≥75.0. Results A panel of 14 experts (11 from Europe, three from America) with a nine years median experience was enrolled. All the statements about feasibility, face and content validity were supported, except for QCT capability to recognize elementary lesions. Conclusions The panel of experts supported feasibility, face, and content validity of QCT assessment concerning CTD-ILD. This may stimulate a greater use in clinical practice and further studies to confirm its discriminative properties and its construct validity.

The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease

2018 ◽  
Vol 13 (1) ◽  
pp. 79-94 ◽  
Author(s):  
Florence Jeny ◽  
Pierre-Yves Brillet ◽  
Young-Wouk Kim ◽  
Olivia Freynet ◽  
Hilario Nunes ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Computed Tomography Imaging ◽  
High Resolution Computed Tomography ◽  
Tomography Imaging
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