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A 62-year-old woman came to our hospital with worsening cough and dyspnea over the preceding week, during which time she had been treated with azithromycin and prednisone for suspected pneumonia. She had no fever, chills, or sweats, but her cough had become productive of clear to blood-tinged phlegm during the interval. Medical history was significant for insulin-dependent diabetes mellitus and OSA. She had quit smoking 44 years earlier and had no history of lung disease. She was a bank teller residing in southeastern Minnesota and described no relevant inhalational or environmental exposures, drug use, aspiration, or travels preceding her illness. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to determine the frequency of thyroid gland involvement in chest CT scans of patients with COVID-19 admitted to university-affiliated hospitals and assess its relationship with the severity of lung involvement and patient survival in 2020. Material and methods: In this retrospective cross-sectional study, 1000 PCR-positive patients with COVID-19 who were referred to University-affiliated Hospital in 2020 and had chest CT performed within 72 hours of admission to the hospital were examined. The data was collected by patient file information and CT findings recorded in the PACS system, including thyroid involvement, the severity of lung involvement, and findings related to the death and recovery of patients. Results: The mean age of the examined patients was 56 years. 525 people (52.5%) were men, and 475 (47.5%) were women. 14% had severe pulmonary involvement, and 9.3% had very severe involvement. Moreover, 15.9 percent of them had deceased. 19.7% had focal thyroid involvement, 14% had diffuse involvement, and 66.3% were healthy subjects. Male gender and older age showed a significant relationship with thyroid gland involvement. The severity of lung involvement, the death rate in patients, and hospitalization in ICU were also significantly related to thyroid gland involvement in patients with COVID. Discussion and conclusion: This study highlights the importance of considering thyroid-gland involvement in the comprehensive management of COVID-19 patients. Routine screening and monitoring of thyroid-function may facilitate earlier detection and appropriate management of thyroid-related complications, potentially improving clinical outcomes. This study suggests that in COVID-19 infection the monitoring of thyroid function is prudent, particularly in cases of more serious disease. [ABSTRACT FROM AUTHOR]

Background and Objective: This study investigated the accuracy of portable ultrasonography in the diagnosis of traumatic thoracic lesions (hemothorax, pneumothorax, contusions and fractures) compared to Computed Tomography scan (CT-scan). Materials and Methods: This descriptive-analytical cross-sectional study was carried out on 50 patients with thoracic trauma to assess the diagnostic value of portable ultrasonography compared to CT scan. First, ultrasonography of the lungs and CT scan of the chest were performed. Then, the sensitivity, specificity, and positive and negative predictive values were investigated to examine the accuracy and precision of ultrasonography compared to CT scan. Results: In this study, the sensitivity, specificity, and positive and negative predictive values of ultrasonography for detecting thoracic lesions and complications following trauma were 56.82%, 100.0%, 100%, and 91.52%, respectively, with a diagnostic accuracy of 92.37%. Conclusion: The findings of the present study suggested that in the initial evaluation of patients with traumatic thoracic injuries, ultrasonography, in addition to being an accessible, simple, low-cost and feasible method for all patients, has a high diagnostic value, especially in diagnosing pneumothorax and sternum fracture. [ABSTRACT FROM AUTHOR]

Introduction: Bronchiectasis is a chronic progressive respiratory disease characterized by permanent dilatation of the bronchi. It is a complex condition with numerous different etiologies, co-morbidities, and a heterogeneous presentation. As we know, there is a lack of studies that describe the differences and compare the characteristics between focal and multifocal bronchiectasis. The aim of this study is to identify differences in clinical characteristics presentation, severity or distribution in focal and multifocal bronchiectasis, and prognostic implications. Methods: 126 patients with computed tomography (CT)-verified bronchiectasis were enrolled. Baseline characteristics that included age, sex, smoking history, and respiratory symptoms were recorded, with special attention paid to hemoptysis appearance, body mass index, and comorbidities. The type of bronchiectasis determined by CT scan and the modified Reiff scores indicating radiological severity were recorded. Patients were divided in two groups (I is focal and II is multifocal). Results: There were no statistically significant differences in age, smoking status, comorbidity, and BMI between the two groups. Multifocality was associated with a significantly higher proportion of females (p = 0.014), the rate of hemoptysis (p = 0.023), and the number of hospitalizations, but not of exacerbations and prevalence of immunodeficiency (p = 0.049). Significantly, a high number of subjects with multifocality had bronchiectasis of moderate severity, and post-infective and asthma-associated phenotypes were the dominant in this group. Unexpectedly, the cystic and varicose radiological phenotype (which need more time to develop) were more dominant in the focal group. The cylindrical phenotype was equally observed in the multifocal and focal groups. Conclusions: Our study suggests that multifocality is not related to age, number of exacerbations, or radiological phenotype, but it seems to be associated with the clinical post-infective phenotype, immunodeficiency, frequent hospitalizations, and severity. Thus, the presence of multifocal bronchiectasis may act as a biomarker of severity and poor outcomes in these patients. [ABSTRACT FROM AUTHOR]

Deep learning is a machine learning technique that has been optimized for image classification and object detection. Deep learning has brought huge advancement to the medical field as it helps to diagnose various diseases through computed tomography (CT) scan or X-ray images. Pneumonia is a respiratory disease, and it is one of the killer diseases that causes numerous death all around the world. In 2019, the outbreak of COVID-19 has increased the number of pneumonia patients tremendously. With the increasing number of patients, the clinical and medical facilities have become insufficient. The lack of doctors and radiologists to diagnose pneumonia has caused a high number of patients to be misdiagnosed. Chest image is one of the most effective methods to diagnose this disease, however, examining the X-ray or CT images requires specialists such as radiologists. Meanwhile, examining chest CT or X-ray images might be subjective as the presence of pneumonia can be unclear in the images. The main objective of this paper is to provide a comprehensive review of recent advancement in the diagnosis of pneumonia with deep learning, including state-of-art methodology, datasets, discussion, challenges, and future improvements. [ABSTRACT FROM AUTHOR]

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case–control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI. [ABSTRACT FROM AUTHOR]

Background Lophomonas blattarum is an emerging protozoan that mostly infects the lower respiratory tract and causes pulmonary lophomoniasis. Radiologic findings in patients with pulmonary lophomoniasis have yet to be studied. Thus, we conducted a registry-based clinical investigation to evaluate the radiologic findings of lophomoniasis. Methods In this cross-sectional study, 34 Lophomonas positive patients were enrolled. Demographic data, relevant characteristics, and radiologic findings of the patients were recorded and analyzed. Results Thirty-four (male=18, female=16) patients with an average age of 52.21±20.48 years old were examined. Radiological findings such as Alveolar consolidation (26.5%), Ground glass opacity (5.9%), Centrilobular nodules (23.5%), Tree -in- bud (38.2%), Cavitation (23.5%), Pleural effusion (23.5%), Interstitial opacity (8.8%), Lymphadenopathy (23.5%), Bronchocele (5.9%), Bronchiectasis (29.4%), Nodules (8.8%) and Mass (11.8%) were obtained, that the frequency of all radiological findings was less than 50%. Conclusion In this study, the most common radiological findings in patients with lophomoniasis were tree-in-bud nodules, alveolar consolidation, bronchiectasis, and centrilobular nodules which were mostly seen in the right lung and its middle and lower lobes. Given that the radiologic findings of this disease are unknown, it can be considered in differential diagnosis. [ABSTRACT FROM AUTHOR]

We aimed to investigate the safety and efficacy of nirmatrelvir/ritonavir (Paxlovid) therapy for hemodialysis-dependent patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Thirteen hemodialysis patients infected with the Omicron variant of SARS-CoV-2 from April 3 to May 30, 2022, were recruited. Laboratory parameters and chest CT (computed tomography) imaging were analyzed. The treatment group included six patients who received 150 mg/100 mg of Paxlovid orally once daily for 5 days, whereas the control group included seven patients who received basic treatment. No serious adverse reactions or safety events were recorded. Four control patients progressed to moderate disease, and none in the treatment group showed progression of chest CT findings (P < 0.05). Paxlovid therapy tended toward early viral clearance and low viral load on Day 8. Moreover, 83.3% of the patients in the treatment group and 57.1% of the patients in the control group turned negative within 22 days. In the Paxlovid treatment group, we found significantly increased levels of lymphocytes (P=0.03) and eosinophils (P=0.02) and decreased levels of D-dimer on Day 8 compared with those on Day 1. Paxlovid therapy showed a potential therapeutic effect with good tolerance in hemodialysis patients. The optimal dose and effectiveness evaluation must be further investigated in a largeer cohort. [ABSTRACT FROM AUTHOR]

Background: We study the characteristics and outcomes in lung cancer patients with COVID-19 Omicron variant infection.Methods: Hospitalized lung cancer patients with advanced-stage disease and laboratory-confirmed COVID-19 Omicron infection were included. Pneumonitis involving at least 25% of lung parenchyma on CT scans, accompanied by symptoms and oxygen saturation below 93%, were criteria for enrollment. Pneumonitis severity was graded using CTCAE v5.0. Treatment included Paxlovid, prednisolone, anticoagulation, and ventilation. Initial data, radiographic findings, and outcomes were compared. Logistic regression was employed to determine risk factors for in-hospital mortality.Results: Fifteen patients (median age: 65 years; 80.0% males) were included. 73.3% improved and were discharged, 20.0% died, and 6.7% remained intubated. Initial symptoms included cough (100.0%), fever (73.3%), and shortness of breath (53.3%). Symptoms resolved in discharged patients. Median fever duration was 3.5 days, and respiratory symptom recovery took 26 days. Three patients died due to respiratory failure from Omicron pneumonia. Lower oxygen saturation, reduced lymphocyte/neutrophil ratio on day 7, and diffuse bilateral lung lesions were poor prognostic factors.Conclusion: This study underscores the importance of prompt intervention and early diagnosis for lung cancer patients infected with the COVID-19 Omicron variant. Lower oxygen saturation, decreased lymphocyte/neutrophil ratio on day 7, and diffuse lung lesions on CT scans were associated with worse outcomes. Clinicians should prioritize timely and comprehensive management to improve survival rates in this population. [ABSTRACT FROM AUTHOR]

Purpose: To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1). Methods: Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power. Results: The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%. Conclusion: This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data. [ABSTRACT FROM AUTHOR]

The diagnosis, prognostication, and differentiation of phenotypes of COPD can be facilitated by CT scan imaging of the chest. CT scan imaging of the chest is a prerequisite for lung volume reduction surgery and lung transplantation. Quantitative analysis can be used to evaluate extent of disease progression. Evolving imaging techniques include micro-CT scan, ultra-high-resolution and photon-counting CT scan imaging, and MRI. Potential advantages of these newer techniques include improved resolution, prediction of reversibility, and obviation of radiation exposure. This article discusses important emerging techniques in imaging patients with COPD. The clinical usefulness of these emerging techniques as they stand today are tabulated for the benefit of the practicing pulmonologist. [ABSTRACT FROM AUTHOR]

The harm associated with imaging abnormalities related to lung cancer screening (LCS) is not well documented, especially outside the clinical trial and academic setting. What is the frequency of invasive procedures and complications associated with a community based LCS program, including procedures for false-positive and benign, but clinically important, incidental findings? We performed a single-center retrospective study of an LCS program at a nonuniversity teaching hospital from 2016 through 2019 to identify invasive procedures prompted by LCS results, including their indication and complications. Among 2,003 LCS participants, 58 patients (2.9%) received a diagnosis of lung cancer and 71 patients (3.5%) received a diagnosis of any malignancy. Invasive procedures were performed 160 times in 103 participants (5.1%), including 1.7% of those without malignancy. Eight invasive procedures (0.4% of participants), including four surgeries (12% of diagnostic lung resections), were performed for false-positive lung nodules. Only 1% of Lung Imaging Reporting and Data System category 4A nodules that proved benign were subject to an invasive procedure. Among those without malignancy, an invasive procedure was performed in eight participants for extrapulmonary false-positive findings (0.4%) and in 19 participants (0.9%) to evaluate incidental findings considered benign but clinically important. Procedures for the latter indication resulted in treatment, change in management, or diagnosis in 79% of individuals. Invasive procedures in those without malignancy resulted in three complications (0.15%). Seventy nonsurgical procedures (6% complication rate) and 48 thoracic surgeries (4% major complication rate) were performed in those with malignancy. The use of invasive procedures to resolve false-positive findings was uncommon in the clinical practice of a nonuniversity LCS program that adhered to a nodule management algorithm and used a multidisciplinary approach. Incidental findings considered benign but clinically important resulted in invasive procedure rates that were similar to those for false-positive findings and frequently had clinical value. [ABSTRACT FROM AUTHOR]

As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives. [ABSTRACT FROM AUTHOR]

COPD is a condition characterized by chronic airflow obstruction resulting from chronic bronchitis, emphysema, or both. The clinical picture is usually progressive with respiratory symptoms such as exertional dyspnea and chronic cough. For many years, spirometry was used to establish a diagnosis of COPD. Recent advancements in imaging techniques allow quantitative and qualitative analysis of the lung parenchyma as well as related airways and vascular and extrapulmonary manifestations of COPD. These imaging methods may allow prognostication of disease and shed light on the efficacy of pharmacologic and nonpharmacologic interventions. This is the first of a two-part series of articles on the usefulness of imaging methods in COPD, and it highlights useful information that clinicians can obtain from these imaging studies to make more accurate diagnosis and therapeutic decisions. [ABSTRACT FROM AUTHOR]

The coronavirus (SARS-CoV-2), which causes severe acute respiratory syndrome (COVID-19), is the most current virus with the potential to kill a substantial number of people around the world. Current procedures are restrictive, time-consuming, inefficient, and obsolete. This is due to their exclusive reliance on the technical expertise of a radiologist or medical consultant. Deep learning and technological advancement have also enabled medical researchers and scientists to investigate various neural networks and algorithms to develop apps, tools, and gadgets that can assist medical radiologists. This paper aims to give an overview of deep learning techniques that have been used in chest radiography of COVID-19 and pneumonia cases, as well as the most important datasets that help researchers test algorithms. [ABSTRACT FROM AUTHOR]

Background: Coccidioidomycosis (cocci) is an endemic fungal disease that can cause asymptomatic or post-symptomatic lung nodules which are visible on chest CT scanning. Lung nodules are common and can represent early lung cancer. Differentiating lung nodules due to cocci from those due to lung cancer can be difficult and lead to invasive and expensive evaluations. Materials and Methods: We identified 302 patients with biopsy-proven cocci or bronchogenic carcinoma seen in our multidisciplinary nodule clinic. Two experienced radiologists who were blinded to the diagnosis read the chest CT scans and identified radiographic characteristics to determine their utility in differentiating lung cancer nodules from those due to cocci. Results: Using univariate analysis, we identified several radiographic findings that differed between lung cancer and cocci infection. We then entered these variables along with age and gender into a multivariate model and found that age, nodule diameter, nodule cavitation, presence of satellite nodules and radiographic presence of chronic lung disease differed significantly between the two diagnoses. Three findings, cavitary nodules, satellite nodules and chronic lung disease, have sufficient discrimination to potentially be useful in clinical decision-making. Conclusions: Careful evaluation of the three obtained radiographic findings can significantly improve our ability to differentiate benign coccidioidomycosis infection from lung cancer in an endemic region for the fungal disease. Using these data may significantly reduce the cost and risk associated with distinguishing the cause of lung nodules in these patients by preventing unnecessary invasive studies. [ABSTRACT FROM AUTHOR]

The objective of our study was to determine organ doses to estimate the lifetime attributable risk (LAR) of cancer incidence related to chest tomography simulations for Radiotherapy Treatment Planning (RTTP) using patient-specific information. Patient data were used to calculate organ doses and effective dose. The effective dose (E) was calculated by two methods. First, to calculate effective dose in a standard phantom, the collected dosimetric parameters were used with the ImPACT CT Patient Dosimetry Calculator and E was calculated by applying related correction factors. Second, using the scanner-derived Dose Length Product, LARs were computed using the US National Academy of Sciences (BEIR VII) model for age- and sex-specific risks at each exposure. DLP, CTDIvol, and scan length were 507 ± 143 mGy.cm, 11 ± 4 mGy, and 47 ± 7 cm, respectively. The effective dose was 10 ± 3 mSv using ImPACT patient dosimetry calculator software and 9 ± 2 mSv using the scanner-derived Dose Length Product. The LAR of cancer incidence for all cancers, all solid cancers and leukemia were 65 ± 29, 62 ± 27, 7 ± 2 cases per 100,000 individuals, respectively. Radiation exposure from the usage of CT for radiotherapy treatment planning (RTTP) causes non-negligible increases in lifetime attributable risk. The results of this study can be used as a guide by physicians to implement strategies based on the As Low As Reasonably Achievable (ALARA) principle that lead to a reduction dose without sacrificing diagnostic information. [ABSTRACT FROM AUTHOR]

Early detection of lung cancer through management of pulmonary nodules (PNs) may reduce lung cancer mortality. We assessed the relationship between PNs and lung cancer. How common are PNs in the Medicare population? What is the rate of lung cancer after detection of PNs? What is the relative proportion of early-stage lung cancer diagnosed after reporting of PNs vs through low-dose CT (LDCT) scan screening? Using the Surveillance Epidemiology and End Results Program-Medicare database, we defined two cohorts: those in the 5% sample with ≥ 12 months of Medicare Parts A and B coverage from 2014 through 2019 (5% sample cohort) and those with a diagnosis of lung cancer from 2015 through 2017 with coverage for the prior 18-month period (lung cancer cohort). We defined PNs as chest CT scans with accompanying codes of 793.11 (International Classification of Diseases [ICD], Ninth Revision) or R91.1 (ICD, Tenth Revision) denoting a solitary PN. Patients in the lung cancer cohort were classified by whether they had undergone LDCT scan screening and whether they had a diagnosis of PN or neither (reference) within 18 months before diagnosis. We compared cancer stage and survival across groups. Of 627,547 patients in the 5% sample cohort, 5.0% demonstrated PNs over median of 5.0 years of follow-up. Cumulative 1- and 2-year lung cancer rates after initial PN diagnosis were 3.2% and 4.7%, respectively. Of 44,194 patients in the lung cancer cohort, 15.7%, 2.9%, and 81.4% were in the PN, LDCT scan, and reference groups, respectively. Of patients in the PN, LDCT scan, and reference groups, 58.1%, 50.3%, and 24.4% respectively, had disease of a localized stage. Among all patients with localized disease, 30.0% and 4.9% were in the PN and LDCT scan and groups, respectively. Three-year lung cancer-specific survival rates were 75.0%, 75.6%, and 49.4% for the PN, LDCT scan, and reference groups. Patients with lung cancer who received a diagnosis after identification of PNs tended to have localized disease. Of all patients with localized disease, almost one-third had PNs that were diagnosed previously, compared with 5% of patients who had undergone LDCT scan screening. PNs represent a relatively common presentation of potentially curable lung cancer. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nonspecific clinical features and imaging findings of COVID-19 may lead to misdiagnosis with other diseases that have specific risks and treatments. Here a patient is reported with Pneumocystis Pneumonia with an undiagnosed HIV disease who was treated for COVID-19 with no response after one week. COVID-19 was diagnosed by CT findings but PCR was negative. Further evaluation for ground glass opacities confirmed AIDS and clinical response to Pneumocystis Pneumonia treatment. [ABSTRACT FROM AUTHOR]

Background: Detection of COVID-19 in cancer patients is challenging due to probable preexisting pulmonary infiltration caused by many infectious and non-infectious etiologies. We evaluated chest CT scan findings of COVID-19 pneumonia in cancer patients and explored its prognostic role in mortality. Methods: We studied 266 COVID-19 patients with a history of cancer diagnosis between 2020 and 2022. Chest CT images were reported based on Radiological Society of North America (RSNA) structural report and the CT score and pattern of involvement were noted. We used multivariate logistic regression models to determine the association between CT scan findings and mortality of the cancer COVID-19 patients. Results: The mean age was 56.48 (± 18.59), and 53% were men. Gastrointestinal (29.3%), hematologic (26.3%), and breast (10.5%) cancers were the most frequent types of cancer. The prevalence of atypical or indeterminate findings in the chest CT was 42.8%. Most radiologic findings were consolidation mixed with ground-glass opacity (44.4%), pleural effusion (33.5%), and pure ground-glass opacity (19.5%). The risk of death was higher among those who had typical chest CT for COVID-19 (OR 3.47; 95% CI 1.14–8.98) and those who had a severity of score higher than 18 (OR 1.89; 95% CI 1.07–3.34). Also, presence of consolidation (P value 0.040), pleural effusion (P value 0.000), centrilobular nodules (P value 0.013), and architectural distortion (P value 0.005) were associated with a poorer prognosis. Conclusion: Less than half of COVID-19 patients with a history of cancer had typical imaging features of COVID-19. Radiologists should be aware of atypical, rare, or subtle chest CT findings in patients with pre-existing cancer. [ABSTRACT FROM AUTHOR]

aim to determine the utility of CT scan as a method to accurately confirm pediatric airway foreign bodies (AFBs), the current gold standard of which is chest X-ray as the primary imaging modality in the investigation screening of AFBs with progression to microlaryngobronchoscopy. Methods: A retrospective cohort study of children diagnosed with suspected AFBs between July 2019 and June 2020 was conducted. The primary outcome of missed AFBs from radiologic investigations was recorded. Results: A total of 226 children with an average age of 1.94 years were included in this study. One hundred and two children were eventually admitted to the hospital for microlaryngobronchoscopy. A total of 89 cases were initially examined by chest X-ray with the diagnosis confirmed in 26 cases. The initial examination was chest CT scan in 105 cases, of which the diagnosis was confirmed in 46 cases. The initial examination was chest CT scan with airway reconstruction in 32 cases, and the diagnosis was confirmed in 17 cases. Patients with negative chest CT scan with airway reconstruction were observed to have resolution of symptoms with no further need for bronchoscopy. Conclusion: Chest CT scan with airway reconstruction had the highest rate of confirmed diagnosis of pediatric AFBs on initial scanning, followed by chest CT scan, and finally chest X-ray with fluoroscopy; there was no missed diagnosis in chest CT scan with airway reconstruction. Chest CT scan with airway reconstruction can accurately and quickly detect AFBs and reduce unnecessary bronchoscopy. [ABSTRACT FROM AUTHOR]

Background & Objectives: Numerous studies have shown that the risk of death is higher in elderly patients with COVID-19. In Iran, due to the growth of the elderly population and the prevalence of underlying diseases, it is necessary to pay attention to this age group of patients. In this study, clinical, radiological, PCR, and laboratory data of elderly patients with COVID-19 were collected and analyzed in two groups of recovered and dead patients. Materials & Methods: This is a retrospective study of 196 consecutive elderly patients with COVID-19 hospitalized from March 21, 2020, to April 3, 2020, and their follow-up until April 23, 2021. Clinical characteristics, laboratory results, PCR, chest CT scans and Hounsfield Unit data were collected and analyzed in two groups of recovered and dead patients. Obtained Data were analyzed using SPSSv22.0 and MATLAB-R2017 software. Results: The results showed that there were no specific symptoms to distinguish the death group from the recovery group. Significant differences were observed between the two groups for red blood cell counts, hemoglobin, MCHC and MCV levels, bilirubin, aminotransferase levels, CRP, white blood cells, serum BNU, creatinine and PT coagulation index. The concentrations of VBG (HCO3) and VBG (PCO2) in the deceased patients suggest compensated respiratory alkalosis. CT sensitivity is 100% for patients in advanced and severe stages of the disease. Conclusion: The patients in the death group had more underlying diseases than the recovered group. COVID-19 patients are more likely to die when they have both diabetes and cardiovascular disease. [ABSTRACT FROM AUTHOR]

Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images. [ABSTRACT FROM AUTHOR]

Background: In the setting of a coronary care unit (CCU), the early detection of pneumonia is of paramount important to prevent severe complications. This study was designed aiming to evaluate the diagnostic accuracy of lung ultrasound (LUS) in the detection of pneumonia and compared with chest X-ray (CXR). Method: We enrolled 110 consecutive patients admitted to the CCU of Federico II University Hospital. Each patient underwent CXR and bedside LUS on admission. The final diagnosis (pneumonia vs. no pneumonia) was established by another clinician reviewing clinical and laboratory data independent of LUS results and possibly prescribing chest contrast-enhanced CT (n = 34). Results: The mean age was 70 ± 11 years old, and 68% were males. Pneumonia was clinically diagnosed in 26 (23%) patients. LUS was positive for pneumonia in 24 patients (sensitivity 92%, specificity 81%). Chest radiography was positive for pneumonia in nine patients (sensitivity 43%, specificity 95%). Using CT scan as a reference, LUS exhibited 92% sensitivity and a specificity of 96%. In ROC curve analysis, the diagnostic accuracy of CXR and LUS for the diagnosis of pneumonia was 0.86 (95% CI 0.77–0.94), which was higher than CXR 0.68 (95% CI 0.55–0.84), p < 0.05. Conclusion: Based on the findings of the present study, the accuracy of LUS in the detection of pneumonia was significantly higher than chest X-ray with comparable sensibility to CT scan. [ABSTRACT FROM AUTHOR]

Introduction: In order to allow the resumption of diving activities after a COVID-19 infection, French military divers are required to undergo a medical fitness to dive (FTD) assessment. We present here the results of this medical evaluation performed 1 month after the infection. Methods: We retrospectively analyzed between April 2020 and February 2021 200 records of divers suspected of COVID-19 contamination. Data collected included physical examination, ECG, blood biochemistry, chest CT scan and spirometry. Results: 145 PCR-positive subjects were included, representing 8.5% of the total population of French military divers. Two divers were hospitalized, one for pericarditis and the other for non-hypoxemic pneumonia. For the other 143 divers, physical examination, electrocardiogram and blood biology showed no abnormalities. However 5 divers (3.4%) had persistent subjective symptoms including fatigability, exertional dyspnea, dysesthesias and anosmia. 41 subjects (29%) had significant decreases in forced expiratory flows at 25-75% and 50% on spirometry (n = 20) or bilateral ground-glass opacities on chest CT scan (n = 24). Only 3 subjects were affected on both spirometry and chest CT. 45% of these abnormalities were found in subjects who were initially asymptomatic or had non-respiratory symptoms. In case of abnormalities, normalization was obtained within 3 months. The median time to return to diving was 45 days (IQR 30, 64). Conclusion: Our study confirms the need for standardized follow-up in all divers after COVID-19 infection and for maintaining a rest period before resuming diving activities. [ABSTRACT FROM AUTHOR]

Spontaneous pneumomediastinum (SPM) associated with SARS-CoV-2 infection is a rare condition but can represent a medical emergency. It is probably related to alveolar damage secondary to SARS-CoV-2 infection, which allows air to escape in the surrounding lung tissue. Cough and airways' barotrauma are also mentioned as contributing mechanisms. Treatment is generally conservative, but surgery may be required in severe cases. This paper presents the case of a 16-year-old girl with COVID-19-associated SPM who was treated conservatively in our department. The clinical course was favorable with resolution of respiratory symptoms and radiological (chest CT scan) image of pneumomediastinum. The patient was discharged 7 days after the confirmation of the initial SP diagnosis with appropriate treatment and recommendations for isolation. The sudden occurrence of chest pain and dyspnea should raise the suspicion of SPM in COVID-19 patients. Close surveillance and proper radiological monitoring are required in such cases. Treatment should be strictly individualized based on clinical course and radiological appearance. [ABSTRACT FROM AUTHOR]

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Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with $3 \times 3$ filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics. [ABSTRACT FROM AUTHOR]

Introduction: Whether diagnostic computed tomography (CT) scans to cardiac implantable electronic devices (CIED) is safe in recent models remains unknown. Methods: A two‐centers observational study. Over 14 years, consecutive 2362 chest CT scans (1666 pacemakers [PMs], 145 cardiac resynchronization therapy PM, 316 implantable cardioverter‐defibrillator, and 233 cardiac resynchronization therapy defibrillator) were interrogated and monitored upon imaging. Results: Electromagnetic interference occurred only in a few old models: InSync 8040 (n = 14), InSync III Marquis (n = 1), and Kappa (n = 4), which resulted no adverse events. Conclusion: CIEDs, especially recent ones, are confirmed safe on chest CT. [ABSTRACT FROM AUTHOR]

Background: Septic pulmonary embolism (SPE) is an unusual condition characterized by the implantation of infected thrombi into the pulmonary vasculature from a variety of infectious sources. This study aimed to illustrate the clinicoradiological features, microbiological spectrum, and clinical course of patients with SPE, as well as to promote the early identification, diagnosis, and prognosis of this unusual disease. Methods: Nineteen patients with SPE collected from the electronic medical records of our hospital were retrospectively reviewed during three years. Results: The study included twelve men and seven women with a mean age of 49 (15– 78). The most common presenting features were fever (79%) and shortness of breath (73.7%). Chronic kidney disease (68.4%) and diabetes (36.8%) were the most common comorbidities. The most common source of infection was venous catheters (58%). Staphylococcus aureus was the most predominant pathogen in about 52.6% of the cases. According to the CT findings, bilateral opacities were detected in all cases, flowing by nodular in 73.9% and cavitations in 57.9%. Central distributions were the most patterns regarding the location of the lesion seen in 47.4% of the patients. All patients received antimicrobial treatment, while 13 cases administered systemic anticoagulant. Most of the patients (73.7%) recovered from their illness, while 26.3% died. The median duration of hospitalization was 11.5 days. Oxygen saturation level and altered mental status were significantly associated with the mortality rate of SPE patients. Conclusion: The study's findings presented that altered mental status and low oxygen saturation are associated with a high mortality rate in SPE patients, especially those requiring critical care. Early diagnosis of an embolic phenomenon to other organ systems like the central nervous system can greatly influence the patient's outcome. [ABSTRACT FROM AUTHOR]

Background: Aortic arch calcification (AoAC) is associated with plaque development and cardiovascular events. We aimed to estimate the predictive value of AoAC for stroke recurrence in patients with embolic stroke of undetermined source (ESUS). Methods: Consecutive patients with ESUS who were admitted to our center between October 2019 and October 2020 and who had a 1-year follow-up of stroke recurrence were retrospectively reviewed. According to our AoAC grading scale (AGS), AoAC was classified into four grades based on chest computed tomography (CT) findings: no visible calcification (grade 0), spotty calcification (grade 1), lamellar calcification (grade 2), and circular calcification (grade 3). Results: Of the 158 patients with ESUS (age, 62.1 ± 14.5 years; 120 men) enrolled, 24 (15.2%) had recurrent stroke within a 1-year follow-up. The Cox regression analysis showed that stroke history [hazard ratio (HR), 4.625; 95% confidence interval (CI), 1.828–11.700, p = 0.001] and AoAC (HR , 2.672; 95% CI , 1.129–6.319; p = 0.025) predicted recurrent stroke. AGS grade 1 was associated with a significantly higher risk of stroke recurrence than AGS grade 0 (HR , 5.033; 95% CI , 1.858–13.635, p = 0.001) and AGS grade 2 plus 3 (HR , 3.388; 95% CI , 1.124–10.206, p = 0.030). In patients with AoAC, receiver operating characteristic (ROC) analysis showed that AGS had a good value in predicting stroke recurrence in patients with ESUS, with an area under curve (AUC) of 0.735 (95% CI = 0.601–0.869, p = 0.005). Conclusions: Aortic arch calcification, especially spotty calcification, had a good predictive value for stroke recurrence in patients with ESUS. [ABSTRACT FROM AUTHOR]

We experienced a case of silent Coronavirus disease 2019 (COVID-19) pneumonia that was found by an optional chest computed tomography (CT) scan during a health check. A 62-year-old man with a present medical history of hypertension visited the health screening center at Tokai University Tokyo Hospital on August 7th, 2020. Prior to entry into the hospital, his body temperature was measured and his history was obtained (called 'COVID-19 triage'), but there were no remarkable findings. Subsequently, patchy ground glass opacities were observed with peripheral distribution in bilateral multiple lobes. Based on this finding COVID-19 pneumonia was highly suspected. Subsequently, a PCR test was positive for COVID-19. Even in health check settings, we should be aware of possible encounters with COVID-19 infections. The high risk of silent spread plays a significant role in the ongoing pandemic. Chest CT scans, which can efficiently identify silent COVID-19 pneumonia, should be performed earlier during health check examinations, at least before gastroendoscopy, which causes significant droplet dispersion. Health check examination providers should not cancel or postpone health checks; rather, it is necessary for them to provide health check examinees with a safe environment with minimal delay in access to recommended health care services. [ABSTRACT FROM AUTHOR]

Background/aim: This study was to describe the clinical characteristics, chest CT image findings, and potential role of T cells immunity in adenovirus positive pneumonia. Materials/methods: In this retrospective study, medical records of 53 adult Adv+ patients who were admitted to the Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, from May 2015 to August 2019 were included. The presence of adenovirus and other respiratory viruses was detected using polymerase chain reaction of throat swabs samples. Clinical features and chest computed tomography (CT) findings were compared between patients with Adv+ pneumonia and Adv+ non-pneumonia. Results: The top 3 most commonly occurring symptoms in Adv+ pneumonia patients were fever (66.7%), cough (63.3%), and tachypnea (16.7%). Patients with Adv+ pneumonia showed significantly higher rates of cough and fever and longer duration of hospitalization than patients with Adv+ non-pneumonia. In the Adv+ pneumonia group, consolidation (73.3%) was the most common imaging finding on chest CT scan, and the likelihood of involvement of bilateral lobes (60%) was high. Classical conspicuous consolidation with surrounding ground-glass opacity was observed in 5 (16.6%) patients with Adv+ pneumonia. Patients with Adv+ pneumonia showed a higher inhibition of T-cell immunity than did patients with Adv+ non-pneumonia, and counts of CD3+, CD4+, and CD8+ T-cells may predict the presence of pneumonia in Adv+ patients. Conclusion: With regard to Adv+ pneumonia, the most frequent symptoms were cough and fever, and the most common CT pattern was consolidation; classical CT findings such as consolidation with surrounding ground-glass opacity could also be observed. Furthermore, our data indicated the incidence of abrogated cellular immunity in patients with Adv+ pneumonia. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 acute respiratory distress syndrome (COVID‐19 ARDS) is a disease that often requires invasive ventilation. Little is known about COVID‐19 ARDS sequelae. We assessed the mid‐term lung status of COVID‐19 survivors and investigated factors associated with pulmonary sequelae. Methods: All adult COVID‐19 patients admitted to the intensive care unit from 25th February to 27th April 2020 were included. Lung function was evaluated through chest CT scan and pulmonary function tests (PFT). Logistic regression was used to identify predictors of persisting lung alterations. Results: Forty‐nine patients (75%) completed lung assessment. Chest CT scan was performed after a median (interquartile range) time of 97 (89–105) days, whilst PFT after 142 (133–160) days. The median age was 58 (52–65) years and most patients were male (90%). The median duration of mechanical ventilation was 11 (6–16) days. Median tidal volume/ideal body weight (TV/IBW) was 6.8 (5.71–7.67) ml/Kg. 59% and 63% of patients showed radiological and functional lung sequelae, respectively. The diffusion capacity of carbon monoxide (DLCO) was reduced by 59%, with a median per cent of predicted DLCO of 72.1 (57.9–93.9) %. Mean TV/IBW during invasive ventilation emerged as an independent predictor of persistent CT scan abnormalities, whilst the duration of mechanical ventilation was an independent predictor of both CT and PFT abnormalities. The extension of lung involvement at hospital admission (evaluated through Radiographic Assessment of Lung Edema, RALE score) independently predicted the risk of persistent alterations in PFTs. Conclusions: Both the extent of lung parenchymal involvement and mechanical ventilation protocols predict morphological and functional lung abnormalities months after COVID‐19. [ABSTRACT FROM AUTHOR]

Chest computed tomography (CT) scan is one of the main modalities to detect COVID-19 infection. Several CT scan findings are the hallmark to rapidly detect suspected COVID-19 infection, therefore timely treatment could be administered. This study aims to describe chest CT scan findings of confirmed COVID-19 patients based on clinical symptom onset patterns. This study is a descriptive study with a retrospective design in confirmed COVID-19 patients. Data regarding characteristics of chest CT scan findings and the patient's history including clinical symptoms onset were collected. There were 123 subjects in this study. The study data were categorized into modified clinical symptom onset patterns which were early, progressive, peak, absorption, and advanced phases. Most of male (57%) and female (43%) patients that were admitted to the hospital were in absorption phase of the disease with fever, cough, and dyspnea as the most prevalent symptoms. Chest CT scan findings of absorption phase included GGO (ground glass opacity) in 28 patients (76%), crazy paving pattern in 18 patients (49%), consolidation in 22 patients (59%), and fibrosis in 31 patients (84%). Chest CT scan findings of confirmed COVID-19 patients vary depending on the phase of the disease. Risk factors and secondary bacterial infection may contribute to long-term persistence of symptoms. [ABSTRACT FROM AUTHOR]

The novel coronavirus outbreak has spread worldwide, causing respiratory infections in humans, leading to a huge global pandemic COVID-19. According to World Health Organization, the only way to curb this spread is by increasing the testing and isolating the infected. Meanwhile, the clinical testing currently being followed is not easily accessible and requires much time to give the results. In this scenario, remote diagnostic systems could become a handy solution. Some existing studies leverage the deep learning approach to provide an effective alternative to clinical diagnostic techniques. However, it is difficult to use such complex networks in resource constraint environments. To address this problem, we developed a fine-tuned deep learning model inspired by the architecture of the MobileNet V2 model. Moreover, the developed model is further optimized in terms of its size and complexity to make it compatible with mobile and edge devices. The results of extensive experimentation performed on a real-world dataset consisting of 2482 chest Computerized Tomography scan images strongly suggest the superiority of the developed fine-tuned deep learning model in terms of high accuracy and faster diagnosis time. The proposed model achieved a classification accuracy of 96.40%, with approximately ten times shorter response time than prevailing deep learning models. Further, McNemar's statistical test results also prove the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. We further present the databases and major future challenges of DL-based COVID-19 detection. The computed tomography of the chest and X-ray images gives a rich representation of the patient's lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the preprocessing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pretrained models, generative models, generic neural networks, etc.). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19 and human mobility estimation to limit its spread. [ABSTRACT FROM AUTHOR]

Background: There is an urgent need for population-based studies on managing patients with pulmonary nodules.Research Question: Is it possible to identify pulmonary nodules and associated characteristics using an automated method?Study Design and Methods: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics.Results: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%).Interpretation: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review. [ABSTRACT FROM AUTHOR]

Purpose: Coronavirus disease 2019 (COVID-19) is a novel, highly contagious infection occurring throughout the world. Real-time polymerase chain reaction (RT-PCR) and computed tomography (CT) scan play a pivotal role in diagnosis. Chest CT although sensitive enough, is less specific in diagnosing COVID-19. The purpose of our study was to compare the sensitivity and specificity of chest CT with real-time polymerase chain reaction (RT-PCR) as reference standard. Methods: RT-PCR and chest CT were performed in patients with suspected COVID-19 disease and diagnostic accuracy of both the modalities was compared. Sensitivity, specificity, positive predictive and negative predictive values of the chest CT were calculated with RT-PCR assay as the reference standard. Results: Out of 100 consecutive patients with suspected COVID-19 disease, RT-PCR assay was positive in 64% of patients and abnormal findings on chest CT were seen in 72% of patients. CT was 79.7% sensitive and 41.7% specific in diagnosing COVID-19 disease with positive predictive and negative predictive values of 70.8% and 53.6% respectively, with RT-PCR as gold standard. CT was more sensitive and less specific than RT-PCR in diagnosing COVID-19 disease (p-value 0.02, k=0.224). Conclusion: Appropriate diagnosis of COVID-19 should be made on the basis of clinical presentation, viral nucleotide testing, contact history, occupational/social exposure and RT-PCR assay. Chest CT should only be employed in case of patients with high clinical suspicion of COVID-19 disease with negative RT-PCR result. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) is a routine procedure for diagnosing using ionization radiation which has hazardous effects especially on sensitive organs. Objective: The aim of this study was to quantify the dose reduction effect of lead apron shielding on the testicular region during routine chest CT scans. Material and Methods: In this measurement study, the routine chest CT examinations were performed for 30 male patients with common lead aprons folded and positioned in testis regions. The patient's mean body mass index (BMI) was 26.2 ± 4.6 kg/m². To calculate the doses at testis region, three thermoluminescent dosimeters (TLD-100) were attached at the top surface of the apron as an indicator of the doses without shielding, and three TLDs under the apron for doses with shielding. The TLD readouts were compared using SPSS software (Wilcoxon test) version 16. Results: The radiation dose in the testicular regions was reduced from 0.46 ± 0.04 to 0.20 ± 0.04 mGy in the presence of lead apron shielding (p < 0.001), the reduction was equal to 56%. Furthermore, the heritable risk probability was obtained at 2.0 ×10-5 % and 4.6 ×10-5 % for the patients using the lead apron shield versus without shield, respectively. Conclusion: Applying common lead aprons as shielding in the testis regions of male patients undergoing chest CT scans can reduce the radiation doses significantly. Therefore, this shield can be recommended for routine chest CT examinations. [ABSTRACT FROM AUTHOR]

In this article, we report a patient referred to the clinic of Imam Khomeini Hospital in Sari, Mazandaran, northern Iran, with a 3‐month complaint of chronic cough and weight loss. According to the chest computed tomography scan, a cavity was detected in the upper lobe of the patient's left lung. The patient had no history of smoking or underlying diseases that predisposed him to cavitation, including tuberculosis. Then, bronchoscopy was performed for the patient, and bronchoalveolar lavage fluid was sent to the Iranian National Registry Center for lophomoniasis, and Lophomonas blattarum was detected in samples. Finally, the patient's symptoms were totally resolved by prescribing third‐daily metronidazole for 2 weeks. [ABSTRACT FROM AUTHOR]

Purpose: COVID-19 is a novel, severely contagious and progressive infection occurring worldwide. The diagnosis of the disease is based on real-time polymerase chain reaction (RT-PCR) and computed tomography (CT) scan, even though they are still controversial methods. Material and methods: We studied 54 patients with suspected COVID-19 and the two mentioned methods were compared with each other. Results: Sensitivity and specificity of the abnormal chest CT scan, ground-glass opacity (GGO), consolidation opacity, and both of GGO and consolidation were also surveyed based on RT-PCR. The results showed that RT-PCR assay was negative in 23 (42.6%) patients and positive in 31 (57.4%) cases. Also, the patients with an abnormal chest CT scan comprised 37 (68.5%). The sensitivity and specificity of abnormal CT scan were 78.6% and 42.3%, respectively, based on the RT-PCR method. Conclusions: Other techniques alongside CT scan and RT-PCR are advocated for accuracy of the COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Namık Kemal Tıp Dergisi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: To observe the clinical course of symptomatic pregnant women diagnosed with or suspected of having COVID‐19. Methods: This study analyzed the clinical and laboratory results of 27 patients with real‐time polymerase chain reaction (RT‐PCR)‐confirmed COVID‐19 and 25 patients with a suspected COVID‐19 diagnosis based on their symptoms and chest computed tomography (CT) findings. The patients' coagulation parameters and acute‐phase reactants were evaluated both before and after treatment. The maternal and neonatal outcomes were also reviewed. Results: The mean duration of hospitalization was 6.1 ± 3 days. The gestational age of the patients ranged from 6w2d to 40w2d. Thirty‐five patients' CT scan findings suggested viral pneumonia. Four patients delivered vaginally, and 10 patients underwent a cesarean section during the study period. Four of the cesarean deliveries were indicated due to COVID‐19 hypoxemia‐related fetal distress. Four patients were admitted to the intensive care unit (ICU) after the cesarean section. Conclusion: Early hospitalization and medical treatment can alleviate symptoms, improve the clinical course and reduce the need for ICU in symptomatic pregnant patients with suspected or confirmed COVID‐19. Chest CT scans are a suitable option for suspected but unconfirmed COVID‐19 infection. [ABSTRACT FROM AUTHOR]

Purpose: The long incubation period and asymptomatic spread of COVID-19 present considerable challenges for health care institutions when patients return to elective surgery. Methods: A retrospective review of the first adult elective cases performed between May 18, 2020 and June 14, 2020, after the end of lockdown was analysed in Belgium to answer the following questions: (1) for the 236 cancelled patients during the outbreak, how easy was rescheduling? (2) How useful was universal RT-PCR testing and chest CT scan for the 211 orthopaedic and trauma admissions? (3) How were surgical difficulty category, number of operations and complications different when compared to the pre-COVID period? (4) How would patients balance the benefit of surgery against the unknown risk of developing COVID-19? Results: Before surgery, blood tests for anaesthesiology and imaging related to the surgical procedure were scheduled prior to universal testing (COVID-19 PCR and chest CT) performed 72–120 hours before surgery. Among the 211 asymptomatic patients who were tested before surgery, six had positive PCR, while no abnormality was found on the chest CT scan of all the patients. With this timing for tests, the 104 patients included in the current study for elective surgery were free of disease before undergoing surgery and remained without COVID-19 after surgery. Among the 366 cancelled patients during the outbreak, only 12% of the patients accepted to proceed with rescheduling immediately. Therefore, this resulted in a 70% reduction for elective surgery and in a 50% reduction for arthroplasties as compared to pre-COVID period. The rate of complications was not increased during the post-COVID period. A portion of patients have confused idea of screening and have difficulty to perceive the new rules of health organization. Conclusions: Resumption of elective surgical procedures appears more difficult for patients than for surgeons with a low percentage of cancelled patients accepting to reschedule surgery. Universal testing allowed securing patients; however, surgeons must explore better patient perceptions regarding COVID-19 to facilitate a fully informed decision in the current period. [ABSTRACT FROM AUTHOR]

Keratoplasty is one of the irreplaceable treatment options for corneal diseases. Currently, there is no evidence to substantiate that harvested corneal grafts from COVID-19 patients can contain SARS-CoV-2 virus and lead to a systemic infection. Although the risk of transmission through corneal stromal tissue is low, it potentially exists. Lack of clinical data, unclear potential of donor-derived infection and non-established recommendations for transplantation during the COVID-19 pandemic have resulted in a dramatic reduction in the number of keratoplasty and cornea donors at ophthalmology departments and eye banks. To eliminate the risk of infection of recipients and medical personnel, we suggest that the blood samples of all donors should be screened with RT-PCR tests and nasopharyngeal swabs should be taken. In addition, a chest CT scan should be performed if the circulation is maintained. Moreover, the donors' clinical and epidemiological medical history must be screened for typical symptoms and potential contact with SARS-CoV-2 carriers to reduce the risk of transmission. The Guidelines of the Eye Bank Association of America (EBAA), Global Alliance of Eye Bank Associations (GAEBA) and European Association of Tissue Banks provide useful recommendations to eliminate the risk of transmission according to previous experiences based on similar viruses. [ABSTRACT FROM AUTHOR]

Objective: We retrospectively analyzed the data of 32 hemodialysis patients with COVID-19 to clarify the epidemiological characteristics of this special population. Method: The data of 32 hemodialysis patients with COVID-19, including epidemiological, demographic, clinical, laboratory, and radiological, were collected from the Blood Purification Department of Wuhan Fourth Hospital from February 3 to 16, 2020. Results: Of the 32 patients, 23 were male, and the median age was 58 years; the median dialysis vintage was 33 months. Two groups were divided according to the patient's primary renal disease: group 1 (16 patients with diabetic nephropathy), group 2 (12 patients with primary glomerulonephritis, 2 with obstructive kidney disease, 1 with hypertensive renal damage, and 1 with polycystic kidney). No significant differences were observed between the two groups in epidemiological characteristics, blood cell counts, and radiological performance. Hemodialysis patients are susceptible to COVID-19 at all ages, and patients with diabetes may be a high-risk population (50%). Common symptoms included fever (15 cases), cough (21 cases), and fatigue (7 cases). The blood lymphocyte count decreased in 84.6% of the patients (median: 0.765 × 109/L). Chest CT revealed ground-glass-like lesions in 18 cases, unilateral lung patchiness in 7 cases, bilateral lung patchiness in 7 cases, and pleural effusion in 2 cases. Conclusion: Only 46.875% of the hemodialysis patients with COVID-19 had fever in the early stage; and diabetics may be the most susceptible population. A decrease in blood lymphocyte count and ground-glass opacity on chest CT scan is beneficial in identifying the high-risk population. [ABSTRACT FROM AUTHOR]

Purpose: Based on the recent literature, chest computed tomography (CT) examination could aid for management of patients during COVID-19 pandemic. However, the role of chest CT in management of COVID-19 patients is not exactly the same for medical or surgical specialties. In orthopaedic or trauma emergency, abdomen, pelvis, cervical, dorsal, and lumbar spine CT are performed to investigate patients; the result is a thoracic CT scan incorporating usually the thorax; however, information about lung parenchyma can be obtained on this thorax CT, and manifestations of COVID-19 can be diagnosed. The objective of our study was to evaluate this role in orthopedic patients to familiarize orthopaedists with the value and limits of thoracic CT in orthopaedic surgery. Materials and methods: Among the 1397 chest CT scans performed during the pandemic period from 1 March 2020 to 10 May 2020, in two centres with orthopaedic surgery, we selected all the 118 thoracic or chest CT performed for patients who presented to the Emergency Department of the hospital with a diagnosis of trauma for orthopaedic surgical treatment. Thirty-nine of these 118 patients were tested with PCR for the diagnosis of COVID-19 infection. Depending on clinical status (symptomatic or non-symptomatic), the information useful for the orthopaedist surgeon and obtained from the Chest CT scan according to the result of the PCR (gold standard) was graded from 0 (no or low value) to 3 (high value). The potential risks of chest CT as exposure to radiation, and specific pathway were analyzed and discussed. A group of patients treated during a previous similar period (1 March 2018 to 15 April 2018) was used as control for evaluation of the increase of CT scanning during the COVID-19 pandemic. Results: Among the 118 patients with chest CT, there were 16 patients with positive COVID-19 chest CT findings, and 102 patients with negative chest CT scan. With PCR results as reference, the sensitivity, specificity, positive predictive value of chest CT in indicating COVID-19 infection were 81%, 93%, and 86%, respectively (p = 0.001). A useful information for the orthopaedic surgeon (graded as 1 for 71 cases, as 2 for 5 cases, and as 3 for 11 cases) was obtained from 118 chest CT scans for 87 (74%) patients, while the CT was no value in 30 (25%) cases, and negative value in one (1%) case. Roughly 20% of the total number of CT scanner performed over the pandemic period was dedicated to COVID-19, but only 2% were for orthopaedic or trauma patients. However, this was ten times higher than during the previous control period of comparison. Conclusion: Although extremely valuable for surgery management, these results should not be overstated. The CT findings studied are not specific for COVID-19, and the positive predictive value of CT will be low unless disease prevalence is high, which was the case during this period. [ABSTRACT FROM AUTHOR]