scholarly journals Accuracy of chest high-resolution computed tomography in diagnosing diffuse cystic lung diseases

2015 ◽  
Vol 46 (4) ◽  
pp. 1196-1199 ◽  
Author(s):  
Nishant Gupta ◽  
Riffat Meraj ◽  
Daniel Tanase ◽  
Laura E. James ◽  
Kuniaki Seyama ◽  
...  
Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 439
Author(s):  
Donato Lacedonia ◽  
Giulia Scioscia ◽  
Angelamaria Giardinelli ◽  
Carla Maria Irene Quarato ◽  
Ennio Vincenzo Sassani ◽  
...  

Transthoracic ultrasound (TUS) is a readily available imaging tool that can provide a quick real-time evaluation. The aim of this preliminary study was to establish a complementary role for this imaging method in the approach of interstitial lung diseases (ILDs). TUS examination was performed in 43 consecutive patients with pulmonary fibrosis and TUS findings were compared with the corresponding high-resolution computed tomography (HRCT) scans. All patients showed a thickened hyperechoic pleural line, despite no difference between dominant HRCT patterns (ground glass, honeycombing, mixed pattern) being recorded (p > 0.05). However, pleural lines’ thickening showed a significant difference between different HRCT degree of fibrosis (p < 0.001) and a negative correlation with functional parameters. The presence of >3 B-lines and subpleural nodules was also assessed in a large number of patients, although they did not demonstrate any particular association with a specific HRCT finding or fibrotic degree. Results allow us to suggest a complementary role for TUS in facilitating an early diagnosis of ILD or helping to detect a possible disease progression or eventual complications during routine clinical practice (with pleural line measurements and subpleural nodules), although HRCT remains the gold standard in the definition of ILD pattern, disease extent and follow-up.


2021 ◽  
pp. 028418512199579
Author(s):  
Simon S Martin ◽  
Delina Kolaneci ◽  
Julian L Wichmann ◽  
Lukas Lenga ◽  
Doris Leithner ◽  
...  

Background High-resolution computed tomography (HRCT) is essential in narrowing the possible differential diagnoses of diffuse and interstitial lung diseases. Purpose To investigate the value of a novel computer-based decision support system (CDSS) for facilitating diagnosis of diffuse lung diseases at HRCT. Material and Methods A CDSS was developed that includes about 100 different illustrations of the most common HRCT signs and patterns and describes the corresponding pathologies in detail. The logical set-up of the software facilitates a structured evaluation. By selecting one or more CT patterns, the program generates a ranked list of the most likely differential diagnoses. Three independent and blinded radiology residents initially evaluated 40 cases with different lung diseases alone; after at least 12 weeks, observers re-evaluated all cases using the CDSS. Results In 40 patients, a total of 113 HRCT patterns were evaluated. The percentage of correctly classified patterns was higher with CDSS (96.8%) compared to assessment without CDSS (90.3%; P < 0.01). Moreover, the percentage of correct diagnosis (81.7% vs. 64.2%) and differential diagnoses (89.2% vs. 38.3%) were superior with CDSS compared to evaluation without CDSS (both P < 0.01). Conclusion Addition of a CDSS using a structured approach providing explanations of typical HRCT patterns and graphical illustrations significantly improved the performance of trainees in characterizing and correctly identifying diffuse lung diseases.


2016 ◽  
Vol 40 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Carlos Gustavo Yuji Verrastro ◽  
Viviane Baptista Antunes ◽  
Dany Jasinowodolinski ◽  
Giuseppe DʼIppolito ◽  
Gustavo de Souza Portes Meirelles

2020 ◽  
Vol 7 (22) ◽  
pp. 1062-1067
Author(s):  
Mrinal Kanti Ghosh ◽  
Priyadarshini Sur ◽  
Mustafijur Rahaman ◽  
Soumitra Kumar Ghosh ◽  
Raman Sau ◽  
...  

2021 ◽  
Vol 8 (2) ◽  
pp. 207
Author(s):  
Krishna Pratap Singh Senger ◽  
Ankita Singh

Background: Interstitial lung diseases (ILD) are a heterogeneous group of non-neoplastic disorders resulting from damage to the lung parenchyma by varying patterns of inflammation and fibrosis. With high-resolution computed tomography (HRCT) the pattern of lung damage can be mapped accurately which may help to identify specific ILD.Methods: 65 diagnosed cases of ILD by HRCT who were admitted to a tertiary care chest hospital, formed the study group. All these patients also underwent histopathological confirmation as per hospital protocol. The study was done over a period from August 2016 to July 2019. Clinical details, chest x-ray, HRCT and histopathological data was collected and analysed using 2x2 table for detecting sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV).Results: For diagnosing ILD like acute interstitial pneumonia (AIP), LIP and RB ILD the HRCT fared equally well in diagnostic utility as compared to histopathological examination. But in certain conditions like non-specific interstitial pneumonia (NSIP) the HRCT performed poorly in terms of PPV as compared to gold standard histopathology. In Bronchiolitis obliterans organizing pneumonia (BOOP) and usual interstitial pneumonia (UIP) again the HRCT performed fairly well as compared to gold standard.Conclusions: HRCT shows good correlation with histopathological diagnosis in identifying a various subtype of ILD and may thus serve a useful non-invasive, imaging biomarker not only for diagnosing a particular ILD but for prognostication and response to treatment.


Author(s):  
T. B. Burmistrova

High-resolution computed tomography made it possible to assess changes in the lungs from the effects of industrial aerosols in the development of interstitial pulmonary diseases of professional and non-professional Genesis in 342 patients: pneumoconiosis, hypersensitive pneumonitis, allergic and fibrosing alveolitis, sarcoidosis, pulmonary tuberculosis. High-resolution computed tomography was an additional method in the diagnosis of various forms of lung diseases.


Author(s):  
Hyun Jae Lee ◽  
Ji Eun Son ◽  
Young Seoub Hong ◽  
Young Ill Lee ◽  
Byung Jin Yeah ◽  
...  

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