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1.
Comput Biol Med ; 145: 105467, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1763671

ABSTRACT

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.


Subject(s)
COVID-19 , Lung Neoplasms , Algorithms , COVID-19/diagnostic imaging , Humans , Machine Learning , Prognosis , Retrospective Studies , Tomography, X-Ray Computed/methods
2.
Radiol Case Rep ; 17(5): 1789-1793, 2022 May.
Article in English | MEDLINE | ID: covidwho-1757766

ABSTRACT

Acute disseminated encephalomyelitis (ADEM) is an acute demyelinating disorder of the central nervous system that is ordinarily monophasic. ADEM can develop following infection or vaccination. Here, we present a 37 y/o male patient with progressive muscle weakness in all limbs along with dysphagia following COVID-19 vaccination. Brain magnetic resonance imaging (MRI) revealed typical imaging findings which presented as multifocal T2-FLAIR signal changes in the corticospinal tract, pons, and temporal lobe with diffusion restriction. Magnetic resonance spectroscopy (MRS) further confirmed the diagnosis by the typical elevation of the Choline and Myoinositol peaks. Neurologic impairments have been reported as the potential side effects of COVID-19 vaccines. Appropriate imaging modalities together with a thorough clinical examination are essential for making a correct diagnosis.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321020

ABSTRACT

Background: Resource allocation for traumatic patients who are positive/negative for COVID-19 challenges the diagnosis. We designed this study to compare the chest CT appearances of COVID-19 patients associated with lung contusion versus patients with lung contusion only, to determine the differentiation capability of CT scan concerning the two conditions. Methods: CT-scans of 9 RT-PCR positive patients of lung contusion due to motor-vehicle-accident (COVID-19 with contusion group) and 16 consecutive patients with lung contusions of comparable severity scores from the pre-COVID-19 era (contusion only group) were revaluated retrospectively and blindly by three radiologists in consensus. The distribution and characteristics of presenting CT-scan findings;including presence, shape and distribution of Ground Glass Opacities and consolidations, presence of subpleural sparing, crazy-paving and Atoll sign. In addition, presence of effusions and cavities were compared between the two groups. Time course of the opacities was compared. Results: Bilateral distribution of opacities was noted in 100% of COVID-19 with contusion and 87.5% of contusion only group. There was no significant difference between Ground Glass Opacities or consolidation shapes (P=0.44 and P=0.66). Both Ground Glass Opacities and consolidations were more diffusely distributed in COVID-19 with contusion, while a predominantly peripheral distribution was more commonly seen in the contusion only group (P=0.03 and P=0.01 respectively). Subpleural sparing was noted in 93.8% of contusion only as compared to 44% of CC group (p=0.04). Appearance resembling Atoll sign was noted in 12.5% of the contusion only groups and none of the COVID-19 with contusion group (P=0.01). Time to resolution was significantly longer in COVID-19 with contusion (15±6 days) comparing to contusion only patients (P=0.02). Conclusion: 'Typical' chest CT findings including bilateral peripheral Ground Glass Opacities and consolidations, also crazy-paving and Atoll signs, as well as less typical findings such as subpleural sparing is seen in both lung contusion and COVID-19 pneumonitis. Time course of the lesions might be a better radiologic discriminator between the two entities.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319046

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has profoundly affected the lives of millions of people. To date, there is no approved vaccine or specific drug to prevent or treat COVID-19, while the infection is spreading at an alarming rate globally. Because the development of effective vaccines or novel drugs could take several months (if not years), repurposing existing drugs is considered a more efficient strategy that could save lives now. Statins constitute a class of lipid-lowering drugs with proven safety profiles and many known beneficial pleiotropic effects. Our previous investigations showed that statins have antiviral effects and are involved in the process of wound healing in the lung. This triggered us to evaluate if statin use reduces mortality in COVID-19 patients. Results: After initial recruitment of 459 patients with COVID-19 (Shiraz province, Iran) and careful consideration of the exclusion criteria, a total of 150 patients, of which 75 received statins, were included in our retrospective study. Cox proportional-hazards regression models were used to estimate the association between statin use and rate of death. After propensity score matching, we found that statin use appeared to be associated with a lower risk of morbidity [HR=0.85, 95% CI=(0.02, 3.93), P =0.762] and lower risk of death [(HR= 0.76;95% CI=(0.16, 3.72), P =0.735)];however, these associations did not reach statistical significance. Furthermore, statin use reduced the chance of being subjected to mechanical ventilation [OR=0.96, 95% CI=(0.61–2.99), P =0.942] and patients on statins had a more normal computed tomography (CT) scan result [OR=0.41, 95% CI= (0.07–2.33), P =0.312]. Conclusions: Although we could not demonstrate a significant association between statin use and a reduction in mortality in patients with COVID19 , we do feel that our results are promising and of clinical relevance and warrant the need for prospective randomized controlled trials and extensive retrospective studies to validate the potential beneficial effects of statin treatment on clinical symptoms and mortality rates associated with COVID-19.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310245

ABSTRACT

Purpose: The lack of enough medical evidence about COVID-19 regarding optimal prevention, diagnosis, and treatment contributes negatively to the rapid increase in the number of cases globally. A chest computerized tomography (CT) scan has been introduced as the most sensitive diagnostic method. Therefore, this research aimed to examine and evaluate the chest CT scan as a screening measure of COVID-19 in trauma patients. Methods: : This cross-sectional study was conducted in Rajaee Hospital in Shiraz from February to May 2020. All patients underwent unenhanced CT with a 16-slice CT scanner. The CT-scans were evaluated in a blinded manner and main CT scan features were described and classified into four groups according to RSNA recommendation. Subsequently, the first two RSNA categories with the highest probability of COVID pneumonia (i.e. typical and indeterminate) were merged into the “positive CT scan group” and those with radiologic features with the least probability of COVID pneumonia into “negative CT scan group”. Results: : Chest CT scan had a sensitivity (68%), specificity (56%), positive predictive value (34.8%), negative predictive value (83.7%), and accuracy (59.3%) in detecting COVID-19 among trauma patients. Also, for the diagnosis of COVID-19 by CT scan in asymptomatic individuals a sensitivity of 100% and a specificity of 66.7% and a negative predictive value of 100% was obtained. Conclusion: Findings of the study indicated that the CT scan's sensitivity and specificity is less effective in diagnosing trauma patients with COVID-19 in comparison to non-traumatic people.

6.
Chin J Traumatol ; 25(3): 170-176, 2022 May.
Article in English | MEDLINE | ID: covidwho-1632163

ABSTRACT

PROPOSE: In this study, we re-assessed the criteria defined by the radiological society of North America (RSNA) to determine novel radiological findings helping the physicians differentiating COVID-19 from pulmonary contusion. METHODS: All trauma patients with blunt chest wall trauma and subsequent pulmonary contusion, COVID-19-related signs and symptoms before the trauma were enrolled in this retrospective study from February to May 2020. Included patients (Group P) were then classified into two groups based on polymerase chain reaction tests (Group Pa for positive patients and Pb for negative ones). Moreover, 44 patients from the pre-pandemic period (Group PP) were enrolled. They were matched to Group P regarding age, sex, and trauma-related scores. Two radiologists blindly reviewed the CT images of all enrolled patients according to criteria defined by the RSNA criteria. The radiological findings were compared between Group P and Group PP; statistically significant ones were re-evaluated between Group Pa and Group Pb thereafter. Finally, the sensitivity and specificity of each significant findings were calculated. The Chi-square test was used to compare the radiological findings between Group P and Group PP. RESULTS: In the Group PP, 73.7% of all ground-glass opacities (GGOs) and 80% of all multiple bilateral GGOs were detected (p < 0.001 and p = 0.25, respectively). Single bilateral GGOs were only seen among the Group PP. The Chi-square tests showed that the prevalence of diffused GGOs, multiple unilateral GGOs, multiple consolidations, and multiple bilateral consolidations were significantly higher in the Group P (p = 0.001, 0.01, 0.003, and 0.003, respectively). However, GGOs with irregular borders and single consolidations were more significant among the Group PP (p = 0.01 and 0.003, respectively). Of note, reticular distortions and subpleural spares were exclusively detected in the Group PP. CONCLUSION: We concluded that the criteria set by RSNA for the diagnosis of COVID-19 are not appropriate in trauma patients. The clinical signs and symptoms are not always useful either. The presence of multiple unilateral GGOs, diffused GGOs, and multiple bilateral consolidations favor COVID-19 with 88%, 97.62%, and 77.7% diagnostic accuracy.


Subject(s)
COVID-19 , Contusions , Lung Injury , Contusions/diagnostic imaging , Humans , Lead , Lung/diagnostic imaging , Lung Injury/diagnostic imaging , Lung Injury/etiology , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296988

ABSTRACT

Purpose: To derive and validate an effective radiomics-based model for differentiation of COVID-19 pneumonia from other lung diseases using a very large cohort of patients. Methods: We collected 19 private and 5 public datasets, accumulating to 26,307 individual patient images (15,148 COVID-19;9,657 with other lung diseases e.g. non-COVID-19 pneumonia, lung cancer, pulmonary embolism;1502 normal cases). Images were automatically segmented using a validated deep learning (DL) model and the results carefully reviewed. Images were first cropped into lung-only region boxes, then resized to 296 by 216 voxels. Voxel dimensions was resized to 1mm3 followed by 64-bin discretization. The 108 extracted features included shape, first-order histogram and texture features. Univariate analysis was first performed using simple logistic regression. The thresholds were fixed in the training set and then evaluation performed on the test set. False discovery rate (FDR) correction was applied to the p-values. Z-Score normalization was applied to all features. For multivariate analysis, features with high correlation (R2>0.99) were eliminated first using Pearson correlation. We tested 96 different machine learning strategies through cross-combining 4 feature selectors or 8 dimensionality reduction techniques with 8 classifiers. We trained and evaluated our models using 3 different datasets: 1) the entire dataset (26,307 patients: 15,148 COVID-19;11,159 non-COVID-19);2) excluding normal patients in non-COVID-19, and including only RT-PCR positive COVID-19 cases in the COVID-19 class (20,697 patients including 12,419 COVID-19, and 8,278 non-COVID-19));3) including only non-COVID-19 pneumonia patients and a random sample of COVID-19 patients (5,582 patients: 3,000 COVID-19, and 2,582 non-COVID-19) to provide balanced classes. Subsequently, each of these 3 datasets were randomly split into 70% and 30% for training and testing, respectively. All various steps, including feature preprocessing, feature selection, and classification, were performed separately in each dataset. Classification algorithms were optimized during training using grid search algorithms. The best models were chosen by a one-standard-deviation rule in 10-fold cross-validation and then were evaluated on the test sets. Results: In dataset #1, Relief feature selection and RF classifier combination resulted in the highest performance (Area under the receiver operating characteristic curve (AUC) = 0.99, sensitivity = 0.98, specificity = 0.94, accuracy = 0.96, positive predictive value (PPV) = 0.96, and negative predicted value (NPV) = 0.96). In dataset #2, Recursive Feature Elimination (RFE) feature selection and Random Forest (RF) classifier combination resulted in the highest performance (AUC = 0.99, sensitivity = 0.98, specificity = 0.95, accuracy = 0.97, PPV = 0.96, and NPV = 0.98). In dataset #3, the ANOVA feature selection and RF classifier combination resulted in the highest performance (AUC = 0.98, sensitivity = 0.96, specificity = 0.93, accuracy = 0.94, PPV = 0.93, NPV = 0.96). Conclusion: Radiomic features extracted from entire lung combined with machine learning algorithms can enable very effective, routine diagnosis of COVID-19 pneumonia from CT images without the use of any other diagnostic test.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296987

ABSTRACT

Objective In this large multi-institutional study, we aimed to analyze the prognostic power of computed tomography (CT)-based radiomics models in COVID-19 patients. Methods CT images of 14,339 COVID-19 patients with overall survival outcome were collected from 19 medical centers. Whole lung segmentations were performed automatically using a previously validated deep learning-based model, and regions of interest were further evaluated and modified by a human observer. All images were resampled to an isotropic voxel size, intensities were discretized into 64-binning size, and 105 radiomics features, including shape, intensity, and texture features were extracted from the lung mask. Radiomics features were normalized using Z-score normalization. High-correlated features using Pearson (R 2 >0.99) were eliminated. We applied the Synthetic Minority Oversampling Technique (SMOT) algorithm in only the training set for different models to overcome unbalance classes. We used 4 feature selection algorithms, namely Analysis of Variance (ANOVA), Kruskal- Wallis (KW), Recursive Feature Elimination (RFE), and Relief. For the classification task, we used seven classifiers, including Logistic Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The models were built and evaluated using training and testing sets, respectively. Specifically, we evaluated the models using 10 different splitting and cross-validation strategies, including different types of test datasets (e.g. non-harmonized vs. ComBat-harmonized datasets). The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were reported for models evaluation. Results In the test dataset (4301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83±0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + RF classifier. In RT-PCR-only positive test sets (3644), similar results were achieved, and there was no statistically significant difference. In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in highest performance of AUC, reaching 0.83±0.01 (CI95%: 0.81-0.85), with sensitivity and specificity of 0.77 and 0.74, respectively. At the same time, ComBat harmonization did not depict statistically significant improvement relevant to non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and LR classifier resulted in the highest performance of AUC (0.80±0.084) with sensitivity and specificity of 0.77 ± 0.11 and 0.76 ± 0.075, respectively. Conclusion Lung CT radiomics features can be used towards robust prognostic modeling of COVID-19 in large heterogeneous datasets gathered from multiple centers. As such, CT radiomics-based model has significant potential for use in prospective clinical settings towards improved management of COVID-19 patients.

9.
Emerg Med Int ; 2021: 4188178, 2021.
Article in English | MEDLINE | ID: covidwho-1325175

ABSTRACT

BACKGROUND: The lack of enough medical evidence about COVID-19 regarding optimal prevention, diagnosis, and treatment contributes negatively to the rapid increase in the number of cases globally. A chest computerized tomography (CT) scan has been introduced as the most sensitive diagnostic method. Therefore, this research aimed to examine and evaluate the chest CT scan as a screening measure of COVID-19 in trauma patients. METHODS: This cross-sectional study was conducted in Rajaee Hospital in Shiraz from February to May 2020. All patients underwent unenhanced CT with a 16-slice CT scanner. The CT scans were evaluated in a blinded manner, and the main CT scan features were described and classified into four groups according to RSNA recommendation. Subsequently, the first two Radiological Society of North America (RSNA) categories with the highest probability of COVID-19 pneumonia (i.e., typical and indeterminate) were merged into the "positive CT scan group" and those with radiologic features with the least probability of COVID-19 pneumonia into "negative CT scan group." RESULTS: Chest CT scan had a sensitivity of 68%, specificity of 56%, positive predictive value of 34.8%, negative predictive value of 83.7%, and accuracy of 59.3% in detecting COVID-19 among trauma patients. Moreover, for the diagnosis of COVID-19 by CT scan in asymptomatic individuals, a sensitivity of 100%, specificity of 66.7%, and negative predictive value of 100% were obtained (p value: 0.05). CONCLUSION: Findings of the study indicated that the CT scan's sensitivity and specificity is less effective in diagnosing trauma patients with COVID-19 compared with nontraumatic people.

10.
Acad Radiol ; 28(10): 1331-1338, 2021 10.
Article in English | MEDLINE | ID: covidwho-1225101

ABSTRACT

OBJECTIVES: To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza. MATERIALS AND METHODS: This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings. RESULTS: The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%. CONCLUSION: Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Cross-Sectional Studies , Female , Humans , Influenza, Human/diagnostic imaging , Influenza, Human/epidemiology , Lung , Male , Observer Variation , Radiologists , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
11.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization ; : 1-10, 2021.
Article in English | Taylor & Francis | ID: covidwho-1099539
12.
Clin Breast Cancer ; 21(1): e136-e140, 2021 02.
Article in English | MEDLINE | ID: covidwho-1064943

ABSTRACT

As the Coronavirus disease 2019 (COVID-19) epidemic begins to stabilize, different medical imaging facilities not directly involved in the COVID-19 epidemic face the dilemma of how to return to regular operation. We hereby discuss various fields of concern in resuming breast imaging services. We examine the concerns for resuming functions of breast imaging services in 2 broad categories, including safety aspects of operating a breast clinic and addressing potential modifications needed in managing common clinical scenarios in the COVID-19 aftermath. Using a stepwise approach in harmony with the relative states of the epidemic, health care system capacity, and the current state of performing breast surgeries (and in compliance with the recommended surgical guidelines) can ensure avoiding pointless procedures and ensure a smooth transition to a fully operational breast imaging facility.


Subject(s)
Breast/diagnostic imaging , COVID-19/prevention & control , Delivery of Health Care/standards , Ambulatory Care Facilities/organization & administration , Ambulatory Care Facilities/standards , Breast/pathology , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/pathology , COVID-19/epidemiology , Delivery of Health Care/methods , Female , Humans , Image-Guided Biopsy , Mammography , Practice Guidelines as Topic , SARS-CoV-2 , Safety
13.
Transl Med Commun ; 6(1): 3, 2021.
Article in English | MEDLINE | ID: covidwho-1045590

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has profoundly affected the lives of millions of people. To date, there is no approved vaccine or specific drug to prevent or treat COVID-19, while the infection is globally spreading at an alarming rate. Because the development of effective vaccines or novel drugs could take several months (if not years), repurposing existing drugs is considered a more efficient strategy that could save lives now. Statins constitute a class of lipid-lowering drugs with proven safety profiles and various known beneficial pleiotropic effects. Our previous investigations showed that statins have antiviral effects and are involved in the process of wound healing in the lung. This triggered us to evaluate if statin use reduces mortality in COVID-19 patients. RESULTS: After initial recruitment of 459 patients with COVID-19 (Shiraz province, Iran) and careful consideration of the exclusion criteria, a total of 150 patients, of which 75 received statins, were included in our retrospective study. Cox proportional-hazards regression models were used to estimate the association between statin use and rate of death. After propensity score matching, we found that statin use appeared to be associated with a lower risk of morbidity [HR = 0.85, 95% CI = (0.02, 3.93), P = 0.762] and lower risk of death [(HR = 0.76; 95% CI = (0.16, 3.72), P = 0.735)]; however, these associations did not reach statistical significance. Furthermore, statin use reduced the chance of being subjected to mechanical ventilation [OR = 0.96, 95% CI = (0.61-2.99), P = 0.942] and patients on statins showed a more normal computed tomography (CT) scan result [OR = 0.41, 95% CI = (0.07-2.33), P = 0.312]. CONCLUSIONS: Although we could not demonstrate a significant association between statin use and a reduction in mortality in patients with COVID19, we do feel that our results are promising and of clinical relevance and warrant the need for prospective randomized controlled trials and extensive retrospective studies to further evaluate and validate the potential beneficial effects of statin treatment on clinical symptoms and mortality rates associated with COVID-19.

14.
Rev Cardiovasc Med ; 21(4): 493-495, 2020 12 30.
Article in English | MEDLINE | ID: covidwho-1005375
17.
Acad Radiol ; 27(7): 1047-1048, 2020 07.
Article in English | MEDLINE | ID: covidwho-320596
19.
Acad Radiol ; 27(8): 1189, 2020 08.
Article in English | MEDLINE | ID: covidwho-245396
20.
Acad Radiol ; 27(7): 1044-1045, 2020 07.
Article in English | MEDLINE | ID: covidwho-155477
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