scholarly journals CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

2020 ◽  
Author(s):  
Lin Luo ◽  
Zhendong Luo ◽  
Yizhen Jia ◽  
Cuiping Zhou ◽  
Jianlong He ◽  
...  
Keyword(s):  
Scoring System ◽  
Posterior Part ◽  
Area Under The Curve ◽  
Lower Lobe ◽  
Image Features ◽  
Wall Thickening ◽  
Imaging Features ◽  
Ct Image ◽  
Scoring Method ◽  
Exposure History

Abstract Background Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.Methods Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).Results Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.Conclusions With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

scholarly journals CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

2020 ◽  
Author(s):  
Lin Luo ◽  
Zhendong Luo ◽  
Yizhen Jia ◽  
Cuiping Zhou ◽  
Jianlong He ◽  
...  
Keyword(s):  
Scoring System ◽  
Posterior Part ◽  
Area Under The Curve ◽  
Lower Lobe ◽  
Image Features ◽  
Wall Thickening ◽  
Imaging Features ◽  
Ct Image ◽  
Scoring Method ◽  
Exposure History

Abstract Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.Methods : Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).Results : Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.Conclusions : With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

scholarly journals CT Differential Diagnosis of COVID-19 and Non-COVID-19 in Symptomatic Suspects: A Practical Scoring Method

2020 ◽  
Author(s):  
Lin Luo ◽  
Zhendong Luo ◽  
Yizhen Jia ◽  
Cuiping Zhou ◽  
Jianlong He ◽  
...  
Keyword(s):  
General Hospital ◽  
Scoring System ◽  
Posterior Part ◽  
Lower Lobe ◽  
Image Features ◽  
Wall Thickening ◽  
Ct Image ◽  
Scoring Method ◽  
History Of ◽  
Exposure History

Abstract BackgroundAlthough typical and atypical CT image findings are reported in current studys, overlapping CT image features with viral pneumonia and other respiratory diseases also make difficulties on exclusion diagnosis. To explore a CT practical scoring system to differentia suspected COVID-19 in general hospital.MethodsThirty confirmed cases of COVID-19 and fourty-three cases of other etiology or clinical confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history and laboratory parameters of all patients were collected. Seven positive signs (posterior part/ lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, bronchial wall thickening) from other pneumonia significant image features were set. Scoring analysis of CT features were compared between the two groups (COVID-19 and non-COVID-19).ResultsOlder age, symptoms of diarrhea, exposure history of Wuhan, lower level of white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than Non- COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of combined CT image features analysis revealed area under the curves (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for Score>4, a sensitivity of 100% and a specificity of 23.26% for Score>0, and a sensitivity of 86.67% and a specificity of 67.44% for score>2.ConclusionsWith a simple and practical scoring system based on the CT image features, we can make a hierarchical diagnosis on COVID-19 and non-COVID-19 with different management suggestion.

scholarly journals Clinical and CT manifestations of coronavirus disease 2019 (COVID-19): comparison of suspected cases of COVID-19 in isolation and non-COVID-19 pneumonia in a single-center study conducted in Beijing, China

2020 ◽  
Author(s):  
Heng Liu ◽  
Weihua Li ◽  
Lei Zhang ◽  
Bing Liu ◽  
Chaoying Qi ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Posterior Part ◽  
Lower Lobe ◽  
Imaging Features ◽  
Group Iii ◽  
Reactive Protein ◽  
Group I ◽  
Laboratory Test Results ◽  
Single Center Study ◽  
Centrilobular Nodule

Abstract Background: The clinical and CT manifestations of COVID-19 pneumonia and non-COVID-19 pneumonia in the same period have not been compared in detail. The purpose of this study is to analyze the clinical and CT manifestations of COVID-19 pneumonia and perform a comparison of those isolated patients for presumed COVID-19 infection and of non-COVID-19 pneumonia in the same period.Methods: 173 patients with pneumonia from January 1, 2020 to March 20, 2020 were retrospectively enrolled and classified into three groups: patients with COVID-19 pneumonia (Group I, N=4), patients in hospital-isolation for presumed COVID-19 pneumonia (Group Ⅱ, N=5), and patients with non-COVID-19 pneumonia (Group III, N=163). Clinical symptoms, laboratory test results and CT imaging features were compared among three groups.Results: Fever and cough were the most common clinical symptoms in the three groups. 30/163 (18.4%) patients were asymptomatic in Group III. Leukopenia, lymphocytopenia, and elevated C-reactive protein was identified in 1 (25%), 1 (25%), and 1 (25%) patient in Group I; 1 (20%), 1 (20%), and 2 (40%) patients in Group II; 10/157 (6.4%), 33/157(21.0%), and 94/136 (69.1%) patients in Group III. Demarcated GGO/mixed GGO, ill-defined GGO/mixed GGO, consolidation, centrilobular nodule, tree-in bud opacity, bilateral involvement, peripheral distribution, posterior part/lower lobe predilection was observed in 3/4 (75%), 2/4 (50%), 4/4 (100%), 2/4 (50%), 0, 3/4 (75%), 3/4 (75%), and 2/4 (50%) patients, respectively in Group I; 1/5 (20%), 5/5 (100%), 4/5 (80%), 4/5 (80%), 3/5 (60%), 4/5 (80%), 2/5 (40%), and 3/5 (60%) patients in Group Ⅱ; 1/163 (0.6%), 87/163 (54.3%), 115/163 (70.6%), 117/163 (71.8%), 95/163 (58.3%), 52/163 (31.9%), 9/163 (5.5%), and 9/163 (5.5%) patients in Group III, respectively.Conclusions: Demarcated GGO and consolidation prefer the diagnosis of COVID-19 pneumonia, whereas ill-defined GGO and consolidation, centrilobular nodule surrounded by GGO, and tree-in-bud opacity are preferred for non-COVID-19 pneumonia. chest CT has potential in early identification of COVID-19 and implementation of isolation for appropriate case.

scholarly journals Combining CT Images and Clinical Features of Four Periods to Predict Whether Patients Have Rectal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yingyin Feng ◽  
Qi Ding ◽  
Chen Meng ◽  
Wenfeng Wang ◽  
Jingjing Zhang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Random Forest ◽  
Area Under The Curve ◽  
Image Features ◽  
Learning System ◽  
Image Feature ◽  
Good Prediction ◽  
Ct Image ◽  
Clinical Indicators ◽  
Prediction Result

In this paper, we mainly use random forest and broad learning system (BLS) to predict rectal cancer. A total of 246 participants with computed tomography (CT) image records were enrolled. The total model in the training set (combined with imaging and clinical indicators) has the best prediction result, with the area under the curve (AUC) of 0.999 (95% confidence internal (CI): 0.996–1.000) and the accuracy of 0.990 (95%CI: 0.976–1.000). Model 3, the general model in the test set, has the best prediction result, with the AUC of 0.962 (95%CI: 0.915–1.000) and the accuracy of 0.920 (95%CI: 0.845–0.995). The results of the model using random forest prediction are compared with those using BLS prediction. It can be found that there is no statistical difference between the two results. Our prediction model combined with image features has a good prediction result, and this image feature is the most important among all features. Consequently, we can successfully predict rectal cancer through a combination of the clinical indicators and the comprehensive indicators of CT image characteristics in four different periods (plain scan, vein, artery, and excretion).

scholarly journals Gastroesophageal Reflux in Children With Idiopathic Bronchiectases: a Pilot Study

2020 ◽  
Author(s):  
Cecilia Mantegazza ◽  
Giulia Rendo ◽  
Marta Pagano ◽  
Salvatore Zirpoli ◽  
Milena Meroni ◽  
...  
Keyword(s):  
Gastroesophageal Reflux ◽  
Predictive Value ◽  
Scoring System ◽  
Area Under The Curve ◽  
Evaluation Study ◽  
Mean Value ◽  
Wall Thickening ◽  
High Resolution Computed Tomography ◽  
Level Of Evidence ◽  
Lung Lobe

Abstract Background:The role of gastroesophageal reflux in bronchiectasis development is still object of discussion. We aimed to characterize gastroesophageal reflux (GER) in children with idiopathic bronchiectases (BC) and to analyse the relation with a morpho-functional High-Resolution Computed Tomography (HRCT) scoring systemMethods: Multiple esophageal impedance-pH (MII-pH) parameters in children with respiratory symptoms with and without BC were compared. In children with BC spirometry was performed and HRCT score was calculated by evaluating in each lung lobe: 1.bronchiectasis-peribronchial wall thickening, 2.mucous plugging, 3.abscess-sacculations, 4.consolidations, 5.others. HRCT score was related to MII-pH results. HRCT score accuracy in predicting pathological MII-pH was evaluated by ROC curve.Results: 20 children with BC and 20 without BC were enrolled. No significant differences were found in any MII-pH parameter between the two groups.Among BC children, 7/20 had a pathological MII-pH and didn’t show difference in respiratory function compared to those without GER. There were no significant correlation between HRCT score and MII-pH parameters but a direct (not significant) correlation with RI (r=0.240 p=0.307), acid refluxes (r=0.022 p=0.925) and SI/SAP (r=0.041 p=0.865). The mean value of the HRCT score in children with BC with pathological MII-pH was higher than in the ones with normal MII-pH (6.571 vs. 4.846, p=0.0929). The Area Under the Curve was 0.736. A HRCT score of 4.5 and 7.5 were associated with a negative predictive value of 86.5% and a positive predictive value of 75% respectively. Conclusions:Children with idiopathic BC had no distinct GER features. HRCT scoring system showed a moderate accuracy in predicting MII-pH results and a value ≤4.5 is rarely associated with a pathological MII-pH.Level of evidence:not properly applicable as it is a non-interventional diagnostic evaluation study.

scholarly journals Prediction of response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma using radiomic features

2020 ◽  
Author(s):  
Jian Jia ◽  
Lingwei Meng ◽  
Guidong Song ◽  
Shibin Sun ◽  
Chuzhong Li ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Stereotactic Radiotherapy ◽  
Quantitative Imaging ◽  
Area Under The Curve ◽  
Image Features ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Imaging Features ◽  
Operating Characteristics ◽  
Nonfunctioning Pituitary Adenoma

Abstract Background: For individually predicting preoperative response to Stereotactic radiotherapy for Nonfunctioning pituitary Adenoma with the use of a radiomics approach.Methods: 93 cases (training set: n = 62; test set: n = 31) were recruited with contrast-enhanced T1-weighted MRI (CE-T1) before stereotactic radiotherapy. All of these patients received another MRI scan to assess sensitivity of radiotherapy after 12 to 18 months. The shrinkage and no increase in tumor volume are regarded as sensitive to gamma knife radiotherapy. According to CE-T1 images, we extracted 1208 quantitative imaging features totally. Support vector machine (SVM) combined with recursive feature elimination (RFE) and grid-search trained a four-feature prediction mode verified with an assay of receiver operating characteristics (ROC) for an individual set of test. In addition, a ROC curves with individual feature and signature bar were constructed for prediction.Results: The cross-validation area under the curve (AUC) on the three-fold train set is 0.991,0.843 and 0.889. In terms of the test and training sets, T1-CE image features led to 0.897 and 0.914 AUC, separately. Conclusions: With the use of a radiomics method, the response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma was primarily predicted before the operation. The built mode performed well, suggesting that radiomics is promising to preoperatively predict sensitivity to radiotherapy in NFPA.

COMPARISION OF DENTAL SCORING SYSTEM WITH RADIOGRAPHIC SKELETAL SYSTEM IN AGE ESTIMATION

JKCD ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 9-11
Author(s):  
Sadaf Ambreen
Keyword(s):  
Age Estimation ◽  
Scoring System ◽  
Skeletal Age ◽  
Chronological Age ◽  
Age Group ◽  
Dental Age ◽  
Scoring Method ◽  
Cross Sectional ◽  
Study Results ◽  
Male Subjects

Objectives: To compare Demirjian Dental scoring method with Greulich-Pyle (GP) Skeletal method of age estimation in pubertal children. Materials and Methods: Sample of the study included 267 male healthy subjects of 11-16 years of age group.. Demirjian Scoring system was utilized to evaluate the orthopantomograms to assess their Dental age and the Hand-Wrist radiographs were analyzed to calculate the skeletal age by utilizing GP atlas. Chronological age was obtained from the date of birth of the subject .Both methods were compared with one another and with the chronological age. It was a cross-sectional study and only healthy male subjects without any clinical abnormalities were included in the study. Results: A total of 267 male subjects of 11-16 years of age group were assessed by Demirjian and Greulich Pyle Methods. Both were compared with Chronological Age. Data obtained was statistically analyzed and the Student “t” test was applied in the study population. The mean difference between Chronolgical age and dental age was 0.69years and that of chronological age and skeletal age was 0.87 years. It was observed from dental age assessment that it does not differ much from the skeletal age. Conclusion: It was concluded that Demirjian method of Age Estimation is more precise than Greulich Pyle method of Age Estimation. Furthermore both methods can be used selectively in Medicolegal cases to access bone age which can be easily correlated to chronological age.

scholarly journals Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

2020 ◽  
Vol 30 (5) ◽  
pp. 746-753
Author(s):  
Ning Dong ◽  
Hulin Piao ◽  
Yu Du ◽  
Bo Li ◽  
Jian Xu ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Type A ◽  
Postoperative Management ◽  
Prediction Score ◽  
Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age &gt;45 years; (ii) body mass index &gt;25 kg/m2; (iii) white blood cell count &gt;13.5 × 109/l; and (iv) lowest perioperative haemoglobin &lt;100 g/l, cardiopulmonary bypass duration &gt;150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

scholarly journals Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yongfeng Gao ◽  
Jiaxing Tan ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Yumei Huo
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Ct Image ◽  
Computer Aided Detection ◽  
Ct Scanner ◽  
Human Visual Perception ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

scholarly journals Chest computed tomography imaging features in patients with coronavirus disease 2019 (COVID-19)

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110106
Author(s):  
Hoda Salah Darwish ◽  
Mohamed Yasser Habash ◽  
Waleed Yasser Habash
Keyword(s):  
Computed Tomography ◽  
Intermediate Phase ◽  
Early Stage ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Intermediate Stage ◽  
Mediastinal Lymphadenopathy ◽  
Imaging Features ◽  
Stage Disease ◽  
Bilateral Lesions

Objective To analyze computed tomography (CT) features of symptomatic patients with coronavirus disease 2019 (COVID-19). Methods Ninety-five symptomatic patients with COVID-19 confirmed by reverse-transcription polymerase chain reaction from 1 May to 14 July 2020 were retrospectively enrolled. Follow-up CT findings and their distributions were analyzed and compared from symptom onset to late-stage disease. Results Among all patients, 15.8% had unilateral lung disease and 84.2% had bilateral disease with slight right lower lobe predilection (47.4%). Regarding lesion density, 49.4% of patients had pure ground glass opacity (GGO) and 50.5% had GGO with consolidation. Typical early-stage patterns were bilateral lesions in 73.6% of patients, diffuse lesions (41.0%), and GGO (65.2%). Pleural effusion occurred in 13.6% and mediastinal lymphadenopathy in 11.5%. During intermediate-stage disease, 47.4% of patients showed GGO as the disease progressed; however, consolidation was the predominant finding (52.6%). Conclusion COVID-19 pneumonia manifested on lung CT scans with bilateral, peripheral, and right lower lobe predominance and was characterized by diffuse bilateral GGO progressing to or coexisting with consolidation within 1 to 3 weeks. The most frequent CT lesion in the early, intermediate, and late phases was GGO. Consolidation appeared in the intermediate phase and gradually increased, ending with reticular and lung fibrosis-like patterns.

Sign in / Sign up

Export Citation Format

Share Document