Objective To investigate the role of chest computed tomography (CT) examinations acquired early after initial onset of symptoms in predicting disease course in coronavirus disease 2019. Methods Two hundred sixty-two patients were categorized according to intensive care unit (ICU) admission, survival, length of hospital stay, and reverse transcriptase-polymerase chain reaction positivity. Mean time interval between the onset of symptoms and CT scan was 5.2 ± 2.3 days. Groups were compared using Student t test, Mann-Whitney U, and Fisher exact tests. Results In the ICU (+) and died groups, crazy paving (64% and 57.1%), bronchus distortion (68% and 66.7%), bronchiectasis-bronchiolectasis (80% and 76.2%), air trapping (52% and 52.4%) and mediastinal-hilar lymph node enlargement (52% and 52.4%) were significantly more encountered (P < 0,05). These findings were correlated with longer hospital stays (P < 0.05). There were no differences between reverse transcriptase-polymerase chain reaction-positive and -negative patients except bronchiectasis-bronchiolectasis. Conclusion Computed tomography examinations performed early after the onset of symptoms may help in predicting disease course and planning of resources, such as ICU beds.

BackgroundThoracic CT imaging is widely used as a diagnostic method in the diagnosis of COVID-19 pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients gained importance, especially during the pandemic period.AimsWe aimed to investigate whether there is a difference between the CT imaging findings characteristically defined in COVID-19 pneumonia and the findings detected in pneumonia due to other viral agents, and which finding may be more effective in the diagnosis.Study DesignThe study included 249 adult patients with pneumonia found in thorax CT examination and positive COVID-19 RT-PCR test and 94 patients diagnosed with non-COVID pneumonia (viral PCR positive, no bacterial/fungal agents were detected in other cultures) from the last 5 years before the pandemic. It was retrospectively analyzed using the PACS System. CT findings were evaluated by two radiologists with 5 and 20 years of experience who did not know to which group the patient belonged, and it was decided by consensus.MethodsDemographic data (age, gender, known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups as non-COVID-19 and COVID-19 pneumonia and compared statistically with chi-square tests and multiple regression analysis of independent variables.ResultsTwo main groups; RSNA and CORADS classification, percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation and mediastinal/hilar lymphadenopathy were compared, significant differences were found between the groups (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect of reverse halo sign (β = 0.097, p <0.05) and pleural effusion (β = 10.631, p <0.05) on COVID-19 pneumonia.ConclusionPresence of reverse halo and absence of pleural effusion was found to be efficient in the diagnosis of COVID-19 pneumonia.

This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODSPre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper-based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTSForty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSIONMachine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy. KEYWORDSArtificial intelligence, human papillomavirus DNA tests, machine learning, radiology, uterine cervical neoplasms C ervical cancer is the fourth most common female cancer and the second most common in women aged 15-44. 1 The etiological factor in more than 95% of cervical cancer cases is human papillomavirus (HPV). [2][3][4] Fifteen of more than 200 oncogene types are identified as high risk, and type-16 and -18 HPV infections are the most common in women with cervical cancer. 5 In addition, several studies in the literature report that HPV DNA status is From the Clinic of Radiology (O.İ. 

Background Hyperinflammation (HI) developing in 2 nd week of COVID‐19 contributes to the worse outcome. Because of relatively milder laboratory findings, available criteria for classification of hemophagocytic lymphohistiocytosis or macrophage activation syndrome could not be helpful. Methods Discovery cohort included symptomatic COVID‐19 patients from Turkey, followed at hospital during the initial wave. Replication cohort consisted of hospitalized patients from a later period; all required oxygen support and received glucocorticoids. Diagnosis of HI was made by an expert panel and the majority received tocilizumab or anakinra. Daily clinical and laboratory data were recorded, and data of treatment start day were compared with the 5 ‐ 6 th day data of other patients. Values maximizing the sensitivity and specificity of each parameter were calculated to determine criteria items. Results 685 patients were analyzed in discovery and 156 in replication cohorts; of whom 150 and 61 received treatment for HI, respectively. Mortality rate was higher in HI patients of discovery cohort (23.3%) compared to the rate of other patients (3.7%), and it was much lower in replication cohort for both groups. The 12‐item criteria were developed to define HI of COVID‐19 (HIC), and score of 35 provided 85.3% sensitivity, 81.7% specificity. The same criteria gave 90.0% sensitivity for HIC in replication cohort, but lower specificity values were observed, due to the inclusion of milder cases of HIC responding only glucocorticoids. Conclusions The new criteria are expected to define patients with HIC better with reasonable sensitivity and specificity and enable us to start treatment as early as possible. This article is protected by copyright. All rights reserved.

Background. Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims. We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods. Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups—non-COVID-19 and COVID-19 pneumonia—and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results. RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy ( p < 0.01 ). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (β = 0.097, p < 0.05 ) and pleural effusion (β = 10.631, p < 0.05 ) on COVID-19 pneumonia patients. Conclusion. The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.