scholarly journals Pitfalls in diagnosis of infiltrative lung disease by CT

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20190036
Author(s):  
Céline Du Pasquier ◽  
Rami Hajri ◽  
Romain Lazor ◽  
Cécile Daccord ◽  
Stacey Gidoin ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
False Positive ◽  
Chest Ct ◽  
Post Processing ◽  
Clinical Context ◽  
Technical Aspects ◽  
Ct Examination ◽  
Satisfaction Of Search

The diagnosis of interstitial lung disease may be challenging, especially in atypical disease. Various factors must be considered when performing and reading a chest CT examination for interstitial lung disease, because each of them may represent a source of misinterpretation. Firstly, technical aspects must be mastered, including acquisition and reconstruction parameters as well as post-processing. Secondly, mistakes in interpretation related to the inaccurate description of predominant features, potentially leading to false-positive findings, as well as satisfaction of search must be avoided. In all cases, clinical context, coexisting chest abnormalities and previous examinations must be integrated into the analysis to suggest the most appropriate differential diagnosis.

A CT Algorithm Can Elevate the Differential Diagnosis of Interstitial Lung Disease by Non-specialists to Equal That of Specialist Thoracic Radiologists

2021 ◽  
Author(s):  
Ali H. Dhanaliwala ◽  
Shweta Sood ◽  
Christina Olivias ◽  
Scott Simpson ◽  
Maya Galperin-Aisenberg ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Interstitial Lung Disease ◽  
Lung Disease

Obstructive Lung Diseases

2016 ◽  
pp. 741-746
Author(s):  
Vivek N. Iyer
Keyword(s):  
Differential Diagnosis ◽  
Interstitial Lung Disease ◽  
General Population ◽  
Lung Disease ◽  
Pulmonary Edema ◽  
Lung Diseases ◽  
Lung Parenchyma ◽  
Obstructive Lung Diseases ◽  
Lung Malignancies

An estimated 1 in 3,000 to 1 in 4,000 persons in the general population have a diagnosis of interstitial lung disease (ILD), and ILDs account for about 15% of all consultations for general pulmonologists. These diseases encompass a group of heterogeneous lung conditions characterized by diffuse involvement of the lung parenchyma and pulmonary interstitium. By convention, infections, pulmonary edema, lung malignancies, and emphysema are excluded, but they should be carefully considered as part of the differential diagnosis.

Interstitial Lung Abnormalities in People With HIV Infection and Uninfected Controls

2020 ◽  
Vol 221 (12) ◽  
pp. 1973-1977 ◽  
Author(s):  
Andreas Ronit ◽  
Thomas Benfield ◽  
Jens Lundgren ◽  
Jørgen Vestbo ◽  
Shoaib Afzal ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Hiv Infection ◽  
Lung Disease ◽  
Chest Ct ◽  
Cross Sectional ◽  
Disease States ◽  
Ct Findings ◽  
Lung Abnormalities ◽  
Sectional Analysis ◽  
Over Time

Abstract Background Chest computed tomography (CT) findings in well-treated people with HIV infection (PWH) remain poorly characterized. Methods Cross-sectional analysis examining interstitial chest CT findings in PWH (n = 754) and uninfected controls (n = 470). Results HIV infection was independently associated with 1.82 (95% CI, 1.18–2.88) and 5.15 (95% CI, 1.72–22.2) higher adjusted odds of any interstitial lung abnormality and findings suspicious for interstitial lung disease, respectively. Conclusions HIV infection was independently associated with interstitial lung abnormalities and findings suspicious for interstitial lung disease. Whether these abnormalities develop into more recognizable disease states over time is unknown but warrants further investigation.

scholarly journals Application of artificial intelligence image-assisted diagnosis system in chest CT examination of COVID-19

2020 ◽  
Author(s):  
Mengqi Zhang ◽  
Yangyang Wang ◽  
Qianyun Ding ◽  
Haiwen Li ◽  
Fu Dai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ct Scan ◽  
Predictive Value ◽  
False Positive ◽  
Chest Ct ◽  
True Positive ◽  
Diagnosis System ◽  
Ct Examination ◽  
Missed Diagnosis ◽  
Assisted Diagnosis

Abstract Purpose The purpose of this study is to evaluate the application efficiency of artificial intelligence (AI) image-assisted diagnosis system in chest CT examination of corona virus disease 2019 (COVID-19). Methods A total of 33 cases of COVID-19 patients who underwent chest CT in Hefei Binhu Hospital between January 2020 and March 2020 were retrospectively included. All patients were tested positive for novel coronavirus nucleic acid by fluorescent reverse transcription-polymerasechain reaction (RT-PCR). The pneumonia screening function of the AI image-assisted diagnosis system was employed for the 103 chest CT examinations of the 33 cases. The diagnosis of four senior radiologists were used as the standard for synchronous under blind state. The sensitivity, specificity, misdiagnosis rate, missed diagnosis rate and other evaluation indexes of the COVID-19 performed by the AI image-assisted diagnosis system were analyzed, and an dynamic evaluation on the CT reexamination was conducted. Results Out of the 103 chest CT examinations, there were 88 cases of true positive, 1 case of false positive, 12 cases of true negative and 2 cases of false negative. The sensitivity was 97.78% (88/90); the specificity was 92.31% (12/13); the positive predictive value was 98.88% (88/89); the negative predictive value was 85.71% (12/14); the accuracy was 97.09% (100/103); the Youden index was 90.09%; the positive likelihood ratio was 12.711 and the negative likelihood ratio was 0.024. There were 790 identified lesions in these CT examinations in total, of which 569 were true positive and 221 were false positive. There were also 64 missed diagnosis markers. The detection rate of all lesions was 89.89% and the rate of false positives was 27.97%. In the last CT scan, the lesion size were smaller and the percentage of lesions in total lung volume along with the mean density of lesions was lower than that of the first CT scan. Conclusion The AI image-assisted diagnosis system has certain clinical application value in the early diagnosis and follow-up evaluation of chest CT examination of COVID-19.

scholarly journals Pre-existing interstitial lung disease is associated with onset of nivolumab-induced pneumonitis in patients with solid tumors: A retrospective analysis

2019 ◽  
Author(s):  
Teppei Yamaguchi ◽  
Junichi Shimizu ◽  
Takaaki Hasegawa ◽  
Yoshitsugu Horio ◽  
Yoshitaka Inaba ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Solid Tumors ◽  
Odds Ratio ◽  
Malignant Tumors ◽  
Checkpoint Inhibitors ◽  
Patient Characteristics ◽  
Chest Ct ◽  
Life Threatening ◽  
Male Sex

Abstract BackgroundNivolumab, an anti-programmed death 1 (PD-1) monoclonal antibody, has shown survival benefit in clinical trials of various malignant tumors. Nivolumab-induced pneumonitis is major immune-related adverse event (irAE) that is occasionally serious and life-threatening. The aim of this study was to examine the association between pre-existing interstitial lung disease (ILD) on chest computed tomography (CT) and nivolumab-induced pneumonitis among different types of solid tumors.MethodsWe retrospectively collected the clinical data of 311 patients who were diagnosed with non-small cell lung cancer (NSCLC), head and neck cancer (HNC), or gastric cancer (GC), and treated with nivolumab monotherapy. Patients who underwent chest CT immediately before starting nivolumab without previous thoracic radiotherapy or other immune checkpoint inhibitors were eligible. We collected baseline patient characteristics and assessed pre-existing ILD on baseline chest CT.ResultsFinally, 188 patients were included in the analysis: 96 patients with NSCLC, 43 patients with HNC, and 49 patients with GC. NSCLC patients had a significantly higher rate of pre-existing ILD compared with HNC/GC patients (P=0.047). Nivolumab-induced pneumonitis occurred in 11.7% (22 of 188), including 14.6% (14 of 96) of NSCLC, and 8.7% (8 of 92) of HNC/GC. Univariate and multivariate logistic regression analyses revealed that pre-existing ILD (odds ratio, 5.92; 95% confidence interval (CI), 2.07–18.54, P=0.0008) and male sex (odds ratio, 5.58; 95% CI, 1.01–104.40, P=0.049) significantly increased the risk of nivolumab-induced pneumonitis.ConclusionOur results indicated that pre-existing ILD and male sex are risk factors for nivolumab-induced pneumonitis in solid tumors.

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