scholarly journals A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246582
Author(s):  
Mutlu Gülbay ◽  
Bahadır Orkun Özbay ◽  
Bökebatur Ahmet Raşit Mendi ◽  
Aliye Baştuğ ◽  
Hürrem Bodur
Keyword(s):  
Differential Diagnosis ◽  
Bayesian Information Criterion ◽  
Ground Glass Opacity ◽  
Information Criterion ◽  
Ground Glass ◽  
Atypical Pneumonia ◽  
Multiple Parameters ◽  
Range Parameter ◽  
Negative Skewness ◽  
Thorax Ct

Purpose To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. Methods In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. Results Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. Conclusion During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

Differential Diagnosis of Ground-Glass Opacity Nodules

CHEST Journal ◽  
2007 ◽  
Vol 132 (3) ◽  
pp. 984-990 ◽  
Author(s):  
Koei Ikeda ◽  
Kazuo Awai ◽  
Takeshi Mori ◽  
Koichi Kawanaka ◽  
Yasuyuki Yamashita ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Ground Glass Opacity ◽  
Ground Glass

scholarly journals Preoperative Prediction of Ki-67 Labeling Index By Three-dimensional CT Image Parameters for Differential Diagnosis Of Ground-Glass Opacity (GGO)

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0129206 ◽  
Author(s):  
Mingzheng Peng ◽  
Fei Peng ◽  
Chengzhong Zhang ◽  
Qingguo Wang ◽  
Zhao Li ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Three Dimensional ◽  
Ground Glass Opacity ◽  
Labeling Index ◽  
Ground Glass ◽  
Ct Image ◽  
Ki 67 ◽  
Preoperative Prediction

Ground-Glass Opacities

Chest Imaging ◽  
2019 ◽  
pp. 423-427
Author(s):  
Juliana Bueno
Keyword(s):  
Differential Diagnosis ◽  
Ground Glass Opacity ◽  
Eosinophilic Pneumonia ◽  
Ground Glass ◽  
Organizing Pneumonia ◽  
Diffuse Lung Disease ◽  
Chronic Eosinophilic Pneumonia ◽  
Alveolar Proteinosis ◽  
Pathologic Processes ◽  
Diffuse Lung

Ground-glass opacity (GGO) is defined at thin-section CT as abnormally increased lung density in which vascular and bronchial margins remain visible. This contrasts with consolidation in which those margins are obscured. In the setting of diffuse lung disease, GGO may be related to airspace filling, interstitial thickening or both. Pathologic processes manifesting as diffuse GGO have widely varied symptoms according to the etiology. The assessment of diffuse GGO is primarily achieved with HRCT. Helpful features in establishing a differential diagnosis include: chronicity, distribution of opacities and ancillary findings. Differential diagnosis of acute GGO includes infection, alveolar hemorrhage and pulmonary edema. Chronic GGO may be seen in hypersensitivity pneumonitis, organizing pneumonia, acute or chronic eosinophilic pneumonia, pulmonary alveolar proteinosis and desquamative interstitial pneumonia (DIP). GGO is a nonspecific HRCT pattern that should always be interpreted in light of acuity of symptoms, specific clinical presentation and laboratory results.

scholarly journals Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19

2021 ◽  
Author(s):  
Diletta Cozzi ◽  
Edoardo Cavigli ◽  
Chiara Moroni ◽  
Olga Smorchkova ◽  
Giulia Zantonelli ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Ground Glass Opacity ◽  
Ground Glass

scholarly journals Differences and similarities of high-resolution computed tomography features between pneumocystis pneumonia and cytomegalovirus pneumonia in AIDS patients

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Chun-Jing Du ◽  
Jing-Yuan Liu ◽  
Hui Chen ◽  
Shuo Yan ◽  
Lin Pu ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Ground Glass Opacity ◽  
Pneumocystis Pneumonia ◽  
Ground Glass ◽  
Wall Thickening ◽  
High Resolution Computed Tomography ◽  
Aids Patients ◽  
Similar Frequency ◽  
Cytomegalovirus Pneumonia ◽  
Ct Features

Abstract Background Accurately differentiating pneumocystis from cytomegalovirus pneumonia is crucial for correct therapy selection in AIDS patients. Hence, the goal of this study was to compare the computerized tomography (CT) features of pneumocystis pneumonia and cytomegalovirus pneumonia in AIDS patients and identify clinical hallmarks to accurately distinguish these two pathologies. Methods A total of 112 AIDS patients (78 with pneumocystis pneumonia and 34 cytomegalovirus pneumonia) at Beijing Ditan Hospital from January 2017 to May 2019 were included in this study. Two experienced chest radiologists retrospectively reviewed CT images for 17 features including ground-glass opacity, consolidation, nodules, and halo sign. Binary logistic regression analyses were conducted to identify the significant parameters that distinguished pneumocystis pneumonia from cytomegalovirus pneumonia. Correlations were analyzed by Pearson or Spearman correlation analyses. Result were considered significant if P < 0.05. Results The presence of consolidation, halo signs, and nodules (all P < 0.05) were significantly more frequent in patients with cytomegalovirus pneumonia than in those with pneumocystis pneumonia. Small nodules (32.5% in cytomegalovirus pneumonia, 6.41% in pneumocystis pneumonia, P < 0.001) without perilymphatic distribution were particularly common in patients with cytomegalovirus pneumonia. Large nodules were not found in any of patients with cytomegalovirus pneumonia. The presence of ground-glass opacity, reticulation, and bronchial wall thickening (all P > 0.05) were common in both groups. Conclusions Analysis of consolidation, nodules, and halo signs may contribute to the differential diagnosis of pneumocystis pneumonia or cytomegalovirus pneumonia. However, some CT features considered typical in one or other diseases appear with similar frequency in both cohorts of AIDS patients. CT features are potentially useful for the differential diagnosis of pneumocystis pneumonia and cytomegalovirus pneumonia in AIDS patients.

scholarly journals Differences and similarities of high-resolution computed tomography features between Pneumocystis pneumonia and Cytomegalovirus pneumonia in AIDS patients

2020 ◽  
Author(s):  
Chunjing Du ◽  
Jingyuan Liu ◽  
Hui Chen ◽  
Shuo Yan ◽  
Lin Pu ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Ground Glass Opacity ◽  
Pneumocystis Pneumonia ◽  
Ground Glass ◽  
Wall Thickening ◽  
High Resolution Computed Tomography ◽  
Aids Patients ◽  
Similar Frequency ◽  
Cytomegalovirus Pneumonia ◽  
Ct Features

Abstract Background: Accurately differentiating pneumocystis from cytomegalovirus pneumonia is crucial for correct therapy selection in AIDS patients. Hence, the goal of this study was to compare the computerized tomography (CT) features of pneumocystis pneumonia and cytomegalovirus pneumonia in AIDS patients and identify clinical hallmarks to accurately distinguish these two pathologies.Methods: A total of 112 AIDS patients (78 with pneumocystis pneumonia and 34 cytomegalovirus pneumonia) at Beijing Ditan Hospital from January 2017 to May 2019 were included in this study. Two experienced chest radiologists retrospectively reviewed CT images for 17 features including ground-glass opacity, consolidation, nodules, and halo sign. Significance was calculated by the chi-square (χ2) test. Binary logistic regression analyses were conducted to identify the significant parameters that distinguished pneumocystis pneumonia from cytomegalovirus pneumonia. Correlations were analyzed by Pearson or Spearman correlation analyses. Result were considered significant if P < 0.05.Results: The presence of consolidation, halo signs, and nodules (all P＜0.05) were significantly more frequent in patients with cytomegalovirus pneumonia than in those with pneumocystis pneumonia. Small nodules (32.5% in cytomegalovirus pneumonia, 6.41% in pneumocystis pneumonia, P＜0.001) without perilymphatic distribution were particularly common in patients with cytomegalovirus pneumonia. Large nodules were not found in any of patients with cytomegalovirus pneumonia. The presence of ground-glass opacity, reticulation, and bronchial wall thickening (all P > 0.05) were common in both groups. Conclusions: Analysis of consolidation, nodules, and halo signs may contribute to the differential diagnosis of pneumocystis pneumonia or cytomegalovirus pneumonia. However, some CT features considered typical in one or other diseases appear with similar frequency in both cohorts of AIDS patients. CT features are potentially useful for the differential diagnosis of pneumocystis pneumonia and cytomegalovirus pneumonia in AIDS patients. Add potential implications

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