| کلیدواژههای انگلیسی مقاله |
COVID-19 ● Influenza, Human ● Artificial intelligence ● Tomography, What&,rsquo s Known There is an overlap between COVID-19 and influenza in clinical presentation. Seasonal influenza may have happened concurrently with the COVID-19 pandemic. The polymerase chain reaction test is robust for influenza, but has low sensitivity for COVID-19. Any technique that can differentiate between these infections could improve patient management. What&,rsquo s New Radiomics feature extraction, conjoined with modern artificial intelligence, has high accuracy in differentiating COVID-19 from H1N1 influenza on chest computed tomography. Radiomics-artificial intelligence techniques can improve the accuracy of computed tomography to differentiate COVID-19 from influenza and empower radiologists with limited experience in chest imaging. IntroductionThe coronavirus disease 2019 (COVID-19) pandemic is the first pandemic of the third decade of the 21st century. The first cases with COVID-19 infection were detected in the Chinese city of Wuhan, in December 2019, presenting with fever, cough, pneumonia, and lymphopenia, and unamenable to usual antibiotics. The etiology was soon detected to be a novel coronavirus, SARS-CoV-2. Despite robust measurements, the infection became pandemic in less than three months, with more than 41 million infected cases and over 1.1 million deaths by the end of October 2020. 1, , 2, Since then, the differentiation between COVID-19 and influenza has remained critical for patient management. Currently, the gold-standard diagnostic test for COVID-19 is considered to be the polymerase chain reaction (PCR) test. Nonetheless, not only is this test unavailable even in many developed countries, but also it is associated with questionable accuracy. In this context, chest computed tomography (CT) scanning continues to be of vital importance for diagnosis. Chest CT has a high sensitivity of about 95% to 97% in the detection of COVID-19 pneumonia. 3, , 4, Despite the very promising sensitivity of chest CT to detect COVID-19 infection, the major limitation of this modality is still its low specificity. 5, Currently, radiologists with limited chest imaging experience cannot differentiate COVID-19 from other viral or bacterial pneumonia conditions with high accuracy. Hence, any technique that can improve the specificity of chest CT can enhance their performance. The low specificity of chest CT to differentiate this pneumonia can be partially attributed to the inability of the human eye to detect subtle radiology findings. Generally, the human eye can identify a few radiology features such as the size, density, borders, and enhancement of lesions. In this context, radiomics has proven itself as a rapidly evolving research field in radiology. The basic concept behind radiomics is the ability of computers and software to detect many more radiology features from medical images. In radiomics, the region of interest is generally selected and segmented by a radiologist in order that many features can be extracted from the segmented area. The extracted feature is then analyzed to detect the best diagnostic feature before artificial intelligence (AI) models are developed for these features. 6, In this study, we sought to determine whether radiomics in tandem with different AI models could improve the specificity of chest CT to differentiate COVID-19 pneumonia from H1N1 pneumonia.Patients and MethodsStudy PopulationThis retrospective study was approved by the Ethics Committee of Arak University of Medical Sciences (No. IR.ARAKMU.REC.1398.339). Written informed consent was obtained from each patient upon admission. The entire study population received standards of care based on the university and national guidelines. The medical records of 850 patients with acute respiratory symptoms admitted to three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran, were reviewed. Patients with COVID-19 (after February 2020) and H1N1 influenza-induced pneumonia (before September 2019), who underwent chest CT were included in this study. The inclusion criterion for patients with COVID-19 was a positive PCR test. The quantitative reverse transcription-polymerase chain reaction (RT-qPCR) assay was performed using the 2019-nCoV Nucleic Acid Diagnostic Kit (Sansure Biotech, Changsha, China), in keeping with the manufacturer&,rsquo s protocol, in LightCycler 96 instruments (Roche Diagnostics, Mannheim, Germany). For patients with H1N1 influenza, the inclusion criterion was a positive respiratory viral panel for influenza according to the RT-qPCR assay. For H1N1 cohort, only patients before September 2019 were selected to avoid any overlap between COVID-19 and influenza. Known patients with chronic lung disease were excluded. Finally, 66 patients, comprising 47 cases with COVID-19 and 19 cases with H1N1 influenza, were included for the final analysis. For the influenza cohort, all PCR-positive and inpatient cases in the mentioned university data set were selected. The same data set had a large population of cases with COVID-19. However, only 47 patients with COVID-19 were included to avoid class imbalance. CT ImagesAll the studied patients underwent chest CT with the standard lung protocol (peak kilovoltage [kVp]=100&,ndash 110, milliampere-seconds [mAs]=24&,ndash 40, thickness=&,lt 1.5 mm, pitch factor=0.8, and matrix=512&,times 512). CT scanning was performed with Siemens (SOMATOM Emotion 16 Slice [DE], Germany), Toshiba (Aquilion 16 Slice, Japan), and GE (Optima 58, 32 Slice, USA) scanners. Pulmonary Lesion SegmentationThe chest CT images of the study population were evaluated by a radiologist (HS) with 15 years of clinical imaging experience, who was blind to the patients&,rsquo diagnoses. After the initial assessment, chest CT images with poor quality and motion artifacts were excluded. Pulmonary lesions in the chest CT images in the lung window were then segmented by the same radiologist using 3D Slicer. 7, If a part of a lesion was ground-glass opacification/opacity, and the other part was consolidation, the lesion was segmented as two different lesions. Patchy lesions attaching to each other were considered a single lesion. Axial CT was used for segmentation. Each lesion was segmented in multiple axial slices, and the segmented areas were added to obtain a 3D volume for each lesion. The one-third central portion of the bronchovascular structures was avoided during the segmentation, whereas the two-thirds peripheral portions of the bronchovascular structures were included within the segmented lesions, if they were encased by parenchymal opacities. Pulmonary fissures were also avoided during the segmentation, and large pulmonary lesions were limited to a single pulmonary lobe. Chronic lesions such as calcifications, fibrosis, cavities, pleural effusions, lymphadenopathy, and atelectasis were diagnosed visually by the radiologist at the segmentation time and were not included in the segmentation. Feature ExtractionFeature extraction was performed with 3D Slicer 7, and PyRadiomics Library 8, (resample size=2,2,2 and binWidth=64). For each lesion, 120 features were extracted (table 1,). The extracted features comprised (Shape 2D and Shape 3D) first-order gray-level dependence matrix (GLDM), grey-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), and neighboring gray-tone-difference matrix (NGTDM). Additionally, redundancy maximum relevance, least absolute shrinkage, and selection operator (LASSO), and principal component analysis (PCA) were used for feature selection and reduction. Feature ClassesFeaturesFirst-Order FeaturesEnergy, Total Energy, Entropy, Minimum, 10th Percentile, 90th Percentile, Maximum, Mean, Median, Interquartile Range, Range, Mean Absolute Deviation, Robust Mean Absolute Deviation, Root Mean Squared, Standard Deviation, Skewness, Kurtosis, Variance, and UniformityShape Features (3D)Mesh Volume, Voxel Volume, Surface Area, Surface Area to Volume Ratio, Sphericity Compactness, Spherical Disproportion, Maximum 3D Diameter, Maximum 2D Diameter (Slice), Maximum 2D Diameter, Maximum 2D Diameter, Major Axis Length, Minor Axis Length, Least Axis Length, Elongation, and FlatnessShape Features (2D)Mesh Surface, Pixel Surface, Perimeter, Perimeter to Surface Ratio, Sphericity Spherical Disproportion, Maximum 2D Diameter, Major Axis Length, Minor Axis Length, and ElongationGray-Level Co-occurrence Matrix FeaturesAutocorrelation, Joint Average, Cluster Prominence, Cluster Shade, Cluster Tendency, Contrast, Correlation, Difference Average, Difference Entropy, Difference Variance, Joint Energy, Joint Entropy, Informational Measure of Correlation 1, Informational Measure of Correlation 2, Inverse Difference Moment, Maximal Correlation Coefficient, Inverse Difference Moment Normalized, Inverse Difference, Inverse Difference Normalized, Inverse Variance, Maximum Probability, Sum Average, Sum Entropy, and Sum of SquaresGray-Level Size-Zone Matrix FeaturesSmall-Area Emphasis, Large-Area Emphasis, Gray-Level Nonuniformity, Gray-Level Nonuniformity Normalized, Size-Zone Nonuniformity, Size-Zone Nonuniformity Normalized, Zone Percentage, Gray-Level Variance, Zone Variance, Zone Entropy, Low Gray-Level Zone Emphasis, High Gray-Level Zone Emphasis, Small-Area Low Gray-Level Emphasis, Small-Area High Gray-Level Emphasis, Large-Area Low Gray-Level Emphasis, and Large-Area High Gray-Level EmphasisGray-Level Run-Length Matrix FeaturesShort-Run Emphasis, Long-Run Emphasis, Gray-Level Nonuniformity, Gray-Level Nonuniformity Normalized, Run Length Nonuniformity, Run Length Nonuniformity Normalized, Run Percentage, Gray-Level Variance, Run Variance, Run Entropy, Low Gray-Level Run Emphasis, High Gray-Level Run Emphasis, Short-Run Low Gray-Level Emphasis, Short-Run High Gray-Level Emphasis, Long-Run Low Gray-Level Emphasis, and Long-Run High Gray-Level Emphasis Neighboring Gray-Tone-Difference Matrix FeaturesCoarseness, Contrast, Busyness, Complexity, and StrengthGray-Level Dependence Matrix FeaturesSmall Dependence Emphasis, Large Dependence Emphasis, Gray-Level Nonuniformity, Dependence Nonuniformity, Dependence Nonuniformity Normalized, Gray-Level Variance, Dependence Variance, Dependence Entropy, Low Gray-Level Emphasis, High Gray-Level Emphasis, Small Dependence Low Gray-Level Emphasis, Small Dependence High Gray-Level Emphasis, Large Dependence Low Gray-Level Emphasis, and Large Dependence High Gray-Level EmphasisTable 1.The list of radiomics features used in this studyMachine-Learning Model DevelopmentDifferent binary classifier machine-learning (ML) models, comprising support-vector machine (SVM), decision tree, k-nearest neighbor (k-NN), Na&,iuml ve Bayes, adaptive boosting (AdaBoost), random forest, and neural network were developed using the extracted features to classify each pulmonary lesion into COVID-19 and H1N1 influenza groups. The performance of the models was tested via 10-fold cross-validation and leave-one-out cross-validation analyses on Orange, Data Mining Toolbox in Python. 9, Additionally, the confusion matrix of the models was evaluated. ML model training and testing were repeated twice, once with the raw extracted features and then with the harmonized features. Feature harmonization was performed to avoid the effect of the different CT scanners on the radiomics result. 10, The features were harmonized by using the combatting batch effect (ComBat) harmonization algorithm. Statistical AnalysisThe differences between H1N1 influenza and COVID-19 groups concerning numerical variables were assessed by using the independent samples t test. The Chi square test was utilized to compare categorical variables between these two groups (sex). A P value of less than 0.05 was considered significant. The statistical analyses were performed with SPSS, version 21. The method of this study is summarized in figure 1,. Figure 1. This image depicts the study design. Influenza and COVID-19 are documented by positive PCR. The chest CT images were obtained with the lung protocol and thickness of less than 1.5 mm. PCR, Polymerase chain reaction CT, Computed tomography ML, Machine learningResultsSeventy-three patients with COVID-19 and H1N1 influenza-induced pneumonia were included in this study. After the initial evaluation of the study population&,rsquo s CT images, seven patients were excluded because of motion artifacts or poor-quality images. Sixty-six patients, comprising 47 cases with COVID-19 and 19 cases with H1N1 influenza were enrolled in this study. The demographic data of these patients are provided in table 2,. Regarding CT scanning on the patients with influenza, the Siemens SOMATOM Emotion 16 Slice (DE) Scanner, and the Toshiba Aquilion 16 Slice CT Scanner were used for 16 and three patients, correspondingly. Concerning CT scanning on the patients with COVID-19, the Siemens SOMATOM Emotion 16 Slice (DE) Scanner, the Toshiba Aquilion 16 Slice CT Scanner, and the GE Optima 58, 32 Slice Scanner were employed for 14, 23, and 10 patients, respectively. The patients in the COVID-19 and H1N1 influenza groups were not significantly different in terms of age and sex (P=0.13 and 0.99, respectively). However, the average time between initial symptoms/hospitalization and chest CT was shorter in the COVID-19 group (P=0.001 and 0.01, respectively) (table 2,). Influenza (n=19)COVID-19 (n=47)P valueAge (mean&,plusmn SD)65.89&,plusmn 15.5059.30&,plusmn 16.330.13SexFemale11 (16.7%)16 (24.2%)0.99Male8 (12.1%)31 (47.0%)Average time between initial symptoms and CT (d)7.5&,plusmn 5.023.7&,plusmn 3.380.001Average time between hospitalization and CT (d)1.9&,plusmn 2.341.1&,plusmn 0.40.01The independent samples t test was used to evaluate the differences between H1N1 influenza and COVID-19 samples for numerical variables (age, the average time between the initial symptoms and CT, and the average time between hospitalization and CT). The Chi square test was used to compare categorical variables between these two groups (sex). A P value of less than 0.05 was considered significant. CT, Computed tomography |