Chest Computed Tomography Features Research Articles

Chest CT features and risk factors for patients with Omicron variant pneumonia: a multicenter retrospective clinical study.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Omicron variant infection has become widespread in China as a result of the alterations in epidemic control and prevention policies. We identified the clinical characteristics and lung computed tomography (CT) imaging characteristics of patients infected during the early stage of the Omicron BA.5 wave in Shanghai to provide a guide to the diagnosis, treatment, and prognosis of infection. Clinical information and lung CT imaging characteristics of patients with Omicron variant infection admitted to three designated hospitals in Shanghai from March to June 2022 were analyzed retrospectively. A total of 958 patients were included in the analysis. Among the patients, 169 (17.64%) had pneumonia confirmed by CT, of whom 70.41% (119/169) had lesions in < 10% of the lung area. Older age, unvaccinated status, and comorbid chronic lung disease, cerebrovascular disease, kidney disease, or Alzheimer`s disease were associated with poor prognosis. In patients with coronavirus disease 2019 (COVID-19) pneumonia, a large lesion size was associated with a poor prognosis. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L were risk factors for pneumonia. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L can be used to identify high-risk individuals who warrant a CT scan to screen for COVID-19 pneumonia, especially during the period of Omicron variant predominance. Concurrently, the importance of immunization should be emphasized to help people withstand the effects of Omicron variant infection.

Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes.

Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.

Clinical, Imaging, and Pathological-Molecular Characteristics Associated with Stage IA Invasive Lung Adenocarcinoma Recurrence After Sub-lobar Resection

Rationale and ObjectivesThis study aimed to investigate the association of clinical, imaging, and pathological-molecular characteristics with the prediction of patient prognosis with stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection. Materials and MethodsThis study assessed 360 patients, including 91 and 269 with and without recurrence 3 years postoperativel, respectively, with stage IA ILADC undergoing preoperative chest computed tomography (CT) scans and subsequent sub-lobar resection at our institution. Their clinical and CT features and histological subtypes and gene mutation status were compared. Binary logistic regression analysis was conducted to identify the independent risk factors for recurrence. An external validation cohort included 113 patients, used to test the model’s efficiency. ResultsFor clinical features, old age, male gender, smokers, and high age-adjusted Charlson comorbidity index (ACCI) were frequently observed in patients with recurrence than those without (all p < 0.05). For CT features, large tumor size, solid-predominant density, spiculation, peripheral fibrosis, type II pleural tag, and pleural adhesion were more common in recurrent patients than non-recurrent ones (all p < 0.05). The regression model revealed old age, large tumor size, solid-predominant density, spiculation, type II pleural tag, and pleural adhesion as independent risk factors for recurrence, with an area under the curve (AUC) of 0.942. The external validation cohort obtained an AUC of 0.958. For phological-molecular features, miropapillary/solid-predominant growth pattern, KRAS, ALK, and NRAS mutation or fusion were more ccommon in the recurrent group, whereas EGFR mutation was more frequent in the non-recurrent group (all p < 0.05). ConclusionClinical and CT features help predict the prognosis of patients with stage IA ILADC after sub-lobar resection and decide for individualized treatment. Moreover, patients with different prognosis demonstrated different pathological-molecular features.

Effect of Model-Based Iterative Reconstruction on Image Quality of Chest Computed Tomography for COVID-19 Pneumonia.

This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR). The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion. The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups (P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups (P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher (P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG (P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels (P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure (P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR. The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

Efficacy of different doses of corticosteroids in treating severe COVID-19 pneumonia

BackgroundTo investigate the efficacy of different doses of corticosteroids in treating severe coronavirus disease 2019 (COVID-19) pneumonia.MethodsBetween May 01, 2023, and June 20, 2023, 48 patients with severe COVID-19 pneumonia were treated at the Department of Respiratory and Critical Care Medicine of Jinan Fourth People's Hospital. The observation group (21 patients) received standard care and high-dose corticosteroids, (high-dose group). The control group (27 patients) received standard care and low-dose corticosteroids (low-dose group). We collected baseline data and recorded inflammatory marker levels after 3 days of treatment, body temperature recovery time, length of stay, and 28-day all-cause mortality. The results of outpatient follow-up were recorded after 1 month.ResultsThere were no significant differences in 28-day mortality and length of stay. The number of days it took for body temperature to return to normal in the high-dose group was less than in the low-dose group. The high-dose group had significantly more reduced inflammatory factors (C-reactive protein (CRP), interleukin-6 (IL-6). A total of 20 discharged patients were given 8–16 mg of methylprednisolone, depending on chest computed tomography (CT) and clinical symptoms after 1 month; in all discharged patients using oral corticosteroids, CT features improved.ConclusionHigh-dose corticosteroids had a significantly positive effect on the reduction of inflammatory factors and shortening body temperature recovery time. In the treatment of severe COVID-19 pneumonia, early administration of high-dose, short-course corticosteroids should be implemented.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Shedding light on the next pandemic path, from outpatient to ICU, the effect of vitamin D deficiency in the SARS-CoV-2 pandemic.

Vitamin D insufficiency is named "the pandemic of our era" by some experts. World Health Organization warns against a "deadlier outbreak" than the COVID-19 pandemic. Critical evidence is hereby for future pandemic prevention, with special emphasis on Vitamin D. A cross-sectional study was conducted with 172 unvaccinated adult participants, who presented to the emergency department. Blood measurements, radiological findings, and demographic features were evaluated in the four categories of "healthy adults, COVID-19 outpatients, hospitalized inpatients on the wards, and in the ICU." Results were statistically significant in association with age, gender, weight, Vitamin D, glucose, urea, creatinine, leucocyte, aspartate transaminase, hemoglobin, C-reactive protein, troponin, platelet/thrombocyte, ferritin, D-dimer, triglycerate, glycated haemoglobin, lactate dehydrogenase measurements, and chest computed tomography features (each p < 0.050). This article presents evidence to support the importance of Vitamin D for global public health. Patients with adequate levels of Vitamin D, glucose, urea, creatinine, leucocyte, aspartate transaminase, hemoglobin, C-reactive protein, troponin, platelet/thrombocyte, ferritin, D-dimer, triglycerate, glycated haemoglobin, lactate dehydrogenase are less likely to be admitted to ICU versus being outpatients. Factors include gender, age, weight, comorbidities, and computed tomography findings. The ultimate goal is to globally minimize preventable burdens of disease and death.

Diagnostic Performance and Reproducibility of the Radiological Society of North America Expert Consensus Statement Regarding COVID-19 Pneumonia

INTRODUCTION: To investigate the interobserver and intraobserver agreement and performance of the North American expert agreement declaration in the assessment of chest computed tomography (CT) findings that are observed in new COVID-19 pneumonia.&#x0D; METHODS: In this retrospective study, conducted from March 15th until April 1st, 2020, 113 patients with suspected COVID-19 infection were enrolled. All patients were investigated using real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CT. Chest CT features were categorized by three radiologists in accordance with the North America Consensus Statement. Characteristic and indeterminate features were considered as a group A. Atypical and negative features were considered as a group B. The interobserver and intraobserver agreement of the imaging features were evaluated along with the sensitivity and specificity of the consensus statement.&#x0D; RESULTS: The study population comprised 113 consecutive patients. Of the 113 patients, 61 were RT-PCR-positive. Group A (category 3+4) constituted 89 patients and group B (category 1+2) included 24 patients. The ICC score for intraobserver and interobserver agreement was 0.996 (95% CI) and 0.971 (95% CI), respectively. Using RT-PCR as a reference standard, the sensitivity, specificity, and negative and positive predictive values of CT findings (group A, B) for COVID-19 pneumonia were 82%, 25%, 56.1%, and 54.1%, respectively.&#x0D; CONCLUSION: The expert agreement declaration of reporting new COVID-19 pneumonia tomography findings is a well-designed, reliable, and reproducible standardized CT reporting language and shows excellent intra and interobserver agreement.

Lung microwave ablation: Post-procedure imaging features and evolution of pulmonary ground-glass nodule-like lung cancer.

To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule (GGN)-like lung cancer and its dynamic evolution over time. From June 2020 to May 2021, 147 patients with 152 GGNs (51 pure GGNs and 101 mixed GGNs, mean size 15.0 ± 6.3 mm) were enrolled in this study. One hundred and forty-seven patients underwent MWA procedures. The imaging characteristics were evaluated at predetermined time intervals: immediately after the procedure, 24-48 h, 1, 3, 6, 12, and ≥18 months (47 GGNs). This study population included 147 patients with 152 GGNs, as indicated by the results: 43.5% (66/152) adenocarcinoma in situ, 41.4% (63/152) minimally invasive adenocarcinoma, and 15.1% (23/152) invasive adenocarcinoma. Immediate post-procedure tumor-level analysis revealed that the most common CT features were ground-glass opacities (93.4%, 142/152), hyperdensity within the nodule (90.7%, 138/152), and fried egg sign or reversed halo sign (46.7%, 71/152). Subsequently, 24-48 h post-procedure, ground-glass attenuations, hyperdensity, and the fried egg sign remained the most frequent CT findings, with incidence rates of 75.0% (114/152), 71.0% (108/152), and 54.0% (82/152), respectively. Cavitation, pleural thickening, and consolidation were less frequent findings. At 1 month after the procedure, consolidation of the ablation region was the most common imaging feature. From 3 to 12 months after the procedure, the most common imaging characteristics were consolidation, involutional parenchymal bands and pleural thickening. At ≥18 months after the procedure, imaging features of the ablation zone revealed three changes: involuting fibrosis (80.8%, 38/47), consolidation nodules (12.8%, 6/47), and disappearance (6.4%, 3/47). This study outlined the anticipated CT imaging characteristics of GGN-like lung cancer following MWA. Diagnostic and interventional radiologists should be familiar with the expected imaging characteristics and dynamic evolution post-MWA in order to interpret imaging changes with a reference image.

Comparison of Chest Computed Tomography (CT) Imaging Patterns and Severity among COVID-19 Patients during the First and Fourth Waves in Addis Ababa, Ethiopia

Background. Studies done globally had shown that chest imaging patterns of Corona virus disease 2019 (COVID-19) infection varied depending on the strains of the virus and the waves of the pandemic. There is no published literature done in Ethiopia to examine whether there is any difference in chest computed tomography (CT) findings of COVID-19 patients during the first and fourth waves. Thus, this study tries to fill the gap of knowledge in that regard. Objective. To describe and compare chest CT scan imaging pattern and assess the predictors of chest CT severity of the first and fourth wave of COVID-19 infection. Methods. An institution-based cross-sectional study was conducted on 200 polymerase chain reaction test confirmed COVID-19 patients who underwent chest CT scan imaging in two diagnostic centers in Addis Ababa city. Pioneer and Wudassie diagnostic centers were selected due to the high case load and availability of well-experienced cardiothoracic radiologists. Data were collected from July 1 to August 3, 2022, using a structured Google form sheet questionnaire. Binary logistic regression was performed, and statistical significance was assessed at a level of significance α = 0.05. Results. Comparatively higher proportion of patients from the first wave had positive chest CT finding than fourth wave (99% vs. 69%). Bilateral lung involvement and lower lobe predilection were seen for both waves of COVID-19. Ground glass opacity and consolidation were the most common CT features for both waves. Delayed chest CT features such as traction bronchiectasis were primarily seen among first-wave patients. Mean global CT severity score was higher for the first-wave patients (13.18 vs. 8.31), and the mean difference is statistically significant ( p &lt; 0.001). Duration of symptoms was a statistically significant predictor of CT severity during the first wave of COVID-19, and patients that presented later than 14 days had 4.12 times higher odds of being in the severe CT score category than those that presented less than 7 days (AOR = 4.12, p = 0.011). There was no statistically significant predictor of CT severity for the fourth wave in this study. Conclusion. Chest CT positivity was comparatively higher for first wave patients. Common features included bilateral involvement, lower lobe involvement, ground glass opacity, and consolidation. Mean chest CT severity was comparatively higher for the first wave than the fourth wave, and the duration of symptoms was a statistically significant predictor of the CT severity for first wave.

Rare primary pulmonary mucosa-associated lymphoid tissue lymphoma misdiagnosed with tuberculosis: A case report.

Primary pulmonary mucosa-associated lymphoid tissue lymphoma (MALToma) is a rare subtype of non-Hodgkin lymphoma with a relatively low incidence rate clinically. Atypical clinical symptoms and nonspecific chest computed tomography features of the disease make it difficult to determine and treatment is delayed. We discuss the diagnosis and treatment of a patient with primary pulmonary MALToma to raise clinicians' awareness of this condition. A 66-year-old male patient with a medical history of tuberculosis has been experiencing progressive exacerbation of respiratory symptoms and nonresponsive treatment without an unclear diagnosis for 5 years. He was transferred to our hospital because a nonspecific soft tissue mass in the right upper lobe of the lung was found on his chest computed tomography. Laboratory results with serum immunofixation electrophoresis showed polyclonal immunoglobulin (Ig) G, IgM, IgA, and λ-light chain on admission. Pathological examination and immunohistochemical staining of lung biopsy revealed a definitive diagnosis of pulmonary MALToma with stage IV. The patient received immunotherapy with anti-CD20 monoclonal antibody (rituximab), and showed significant clinical improvement at the 6-month follow-up. Diagnosis of primary pulmonary MALToma mainly relies on histopathological examination, and comprehensive laboratory examinations are also necessary. Clinicians should combine laboratory tests (such as immunofixation electrophoresis in our case) to assist in medical diagnosis in cases of atypical clinical manifestations and imaging characteristics. Immunotherapy appears to be the main treatment protocol for advanced patients.

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia

Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. A total of 2,907 chest CT scans were included in the training (n= 1,934), validation (n= 408), and performance (n= 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4%and 37.1%in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93%and 86%, respectively, and in the multicenter ILD clinical cohort with 81%and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

Evaluation of CT features for differentiating consolidation pattern of pulmonary MALT lymphoma from pneumonic-type lung adenocarcinoma.

In clinical practice, the consolidation pattern of pulmonary mucosa-associated lymphoid tissue (C-MALT) was often misdiagnosed as pneumonic-type lung adenocarcinoma (P-LADC). However, the mainstay of treatment and prognosis of these two diseases are different. The purpose of this study was to distinguish C-MALT from P-LADC by pre-treatment chest computed tomography (CT) features. A total of 31 patients with C-MALT (15 men and 16 women; mean age, 61.1 ± 11.2 years) and 58 patients with P-LADC (34 men and 24 women; mean age, 68.6 ± 7.4 years) confirmed by pathology who underwent contrast-enhanced chest CT were retrospectively enrolled from September 2014 to February 2023. Detailed clinical and CT characteristics of the two groups were evaluated. Logistic regression analysis was used to assess the effectiveness of statistically significant variables in distinguishing C-MALT from P-LADC. The average age of C-MALT was younger than P-LADC patients (p<0.001). With regard to CT features, bronchiectasis within the consolidation was more common in the C-MALT group than the P-LADC group [83.87% (26 of 31) vs 20.69% (12 of 58), p<0.001]; whereas lymph nodes enlargement [75.86% (44 of 58) vs 9.68% (3 of 31), p<0.001] and pleural effusion [43.10% (25of 58) vs 19.35% (6 of 31), p=0.025] were more frequently observed in the P-LADC group than C-MALT group. The predictors with p<0.05 (age, bronchiectasis, lymph node enlargement, and pleural effusion) were used to construct a logistic regression model in discriminating C-MALT from P-LADC, the area under curve (AUC), positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, and accuracy were 0.9555, 86.67%, 91.53%, 83.87%, 93.10%, and 89.89%, respectively. C-MALT and P-LADC have differential clinical and CT features. An adequate understanding of these different characteristics can contribute to the early accurate diagnosis of C-MALT and provide an appropriate therapeutic strategy.

Chest CT features and functional correlates of COVID-19 at 3 months and 12 months follow-up

Long-term pulmonary sequelae of Coronavirus 2019 (COVID-19) remain unclear. Thus, we aimed to establish post-COVID-19 temporal changes in chest computed tomography (CT) features of pulmonary fibrosis and to investigate associations with respiratory symptoms and physiological parameters at 3 and 12 months' follow-up. Adult patients who attended our initial COVID-19 follow-up service and developed chest CT features of interstitial lung disease, in addition to cases identified using British Society of Thoracic Imaging codes, were evaluated retrospectively. Clinical data were gathered on respiratory symptoms and physiological parameters at baseline, 3 months, and 12 months. Corresponding chest CT scans were reviewed by two thoracic radiologists. Associations between CT features and functional correlates were estimated using random effects logistic or linear regression adjusted for age, sex and body mass index. In total, 58 patients were assessed. No changes in reticular pattern, honeycombing, traction bronchiectasis/bronchiolectasis index or pulmonary distortion were observed. Subpleural curvilinear lines were associated with lower odds of breathlessness over time. Parenchymal bands were not associated with breathlessness or impaired lung function overall. Based on our results, we conclude that post-COVID-19 chest CT features of irreversible pulmonary fibrosis remain static over time; other features either resolve or remain unchanged. Subpleural curvilinear lines do not correlate with breathlessness. Parenchymal bands are not functionally significant. An awareness of the different potential functional implications of post-COVID-19 chest CT changes is important in the assessment of patients who present with multi-systemic sequelae of COVID-19 infection.

Evaluation of the RSNA consensus: is it sufficient for the diagnosis of COVID-19 with CT?

Purpose: Radiological Society of North America (RSNA) Consensus for coronavirus disease 19 (COVID-19) is developed to evaluate the lung involvement on chest computed tomography (CT) and create a common reporting lexicon. Aim of this study is to determine the frequency of CT features in sex and age groups in patients with COVID-19, compare the findings according to the RSNA consensus classifications, and evaluate the compatibility of the classifications and findings.&#x0D; Materials and Methods: Chest CT images of 281 patients with COVID-19 were evaluated. Patients were noted in the appropriate RSNA consensus class. The patients’ data were analyzed by group according to age and sex.&#x0D; Results: The main findings included ground-glass opacity, consolidation, and air bronchogram. The common involvement patterns were as follows: bilateral, peripheral, and multifocal. The rates for the typical, atypical, and indeterminate classifications, according to the RSNA consensus, were 63.6%, 9.6%, and 27.0%, respectively. Subpleural fibrous streaking was more frequent in males. Air bronchogram, lymphadenopathy, pleural effusion, subpleural fibrous streaking, bilateral involvement, and a typical classification on CT features were more frequent in the ≥ 65-year age group.&#x0D; Conclusion: While the typical appearance classification has results consistent with the findings, we think that the classifications specified as indeterminate and atypical appearance do not show sufficient agreement with the findings and revision is needed for correct diagnostic guidance.

Could initial CT chest manifestation in patients hospitalized with COVID 19 pneumonia predict outcome on short term basis.

Chest computed tomography (CT) can be used to monitor the course of the disease or response to therapy. Therefore, our study was designed to identify chest CT manifestations that can predict the outcome of patients on short term follow-up. This was a retrospective study wherein we reviewed chest CT scans of 112 real-time reverse transcription polymerase chain reaction positive patients admitted to our hospital. All 112 patients underwent follow-up chest CT at a time interval of 4 to 42 days. Our study included 83 male and 29 female who were positive for COVID 19 infection and admitted to the hospital with positive chest CT findings. All patients underwent follow-up chest CT, and the outcomes were categorized as resolution, regression, residual fibrosis, progression, or death. These proportions were 5.4%, 48.2%, 24.1%, 14.3%, and 8%, respectively. The only significant factor in determining the complete resolution of chest CT was oligo-segmental affection (P = .0001). The main CT feature that significantly affected the regression of chest CT manifestations was diffuse nodular shadows (P = .039). The CT features noted in patients with residual fibrosis were interstitial thickening, with a P value of .017. The mono-segmental process significantly affected progression (P = .044). The significant factors for fatality were diffuse crazy paving, pleural effusion, and extra-thoracic complications (P = .033, .029, and .007, respectively). The prognostic value of the first admission CT can help assess disease outcomes in the earliest phases of onset. This can improve resource distribution.

PET/CT imaging of giant primary pulmonary meningioma: a case report and literature review

BackgroundAn ectopic meningioma, such as a primary pulmonary meningioma (PPM), is a rare type of tumor that primarily originates outside of the central nervous system. The most common presentation of PPM is isolated pulmonary nodules or masses, and most of them are benign. Only sporadic cases have been reported. This case reported a giant primary pulmonary meningioma and systematically reviewed previously reported cases in the literature.Case presentationA 55-year-old female suffered from asthma after activity, chest tightness, and a persistent dry cough for 2 months. Chest computed tomography (CT) showed a huge mass with calcification in the left lower lobe. And positron emission tomography (PET)/CT revealed mild FDG accumulation of the mass. The mass was finally surgically removed and PPM was confirmed according to histopathologic examinations.ConclusionPPM is a rare disease with heterogeneity not only in CT features but also in glucose metabolism. FDG uptake levels do not identify benign from malignant, benign PPM may have high FDG uptake and malignant may have low.

Clinical and chest CT features of immune checkpoint inhibitor-related pneumonitis

Objective: To explore the clinical and chest computed tomography (CT) features and the outcome of immune checkpoint inhibitor-related pneumonitis (CIP). Methods: Clinical and chest CT data of 38 CIP patients with malignant tumors from the Cancer Hospital, Chinese Academy of Medical Sciences between August 2017 and April 2021 were retrospectively reviewed, and the outcomes of pneumonitis were followed up. Results: The median time from the administration of immune checkpoint inhibitors (ICIs) to the onset of CIP was 72.5 days in 38 patients with CIP, and 22 patients developed CIP within 3 months after the administration of ICIs. The median occurrence time of CIP in 24 lung cancer patients was 54.5 days, earlier than 119.0 days of non-lung cancer patients (P=0.138), with no significant statistical difference. 34 patients (89.5%) were accompanied by symptoms when CIP occurred. The common clinical symptoms were cough (29 cases) and dyspnea (27 cases). The distribution of CIP on chest CT was asymmetric in 31 cases and symmetrical in 7 cases. Among the 24 lung cancer patients, inflammation was mainly distributed ipsilateral to the primary lung cancer site in 16 cases and diffusely distributed throughout the lung in 8 cases. Ground glass opacities (37 cases) and consolidation (30 cases) were the common imaging manifestations, and organizing pneumonia (OP) pattern (15 cases) was the most common pattern. In 30 CIP patients who were followed up for longer than one month, 17 cases had complete absorption (complete absorption group), and 13 cases had partial absorption or kept stable (incomplete absorption group). The median occurrence time of CIP in the complete absorption group was 55 days, shorter than 128 days of the incomplete absorption group (P=0.022). Compared with the incomplete absorption group, there were less consolidation(P=0.010) and CIP were all classified as hypersensitivity pneumonitis (HP) pattern (P=0.004) in the complete absorption group. Conclusions: CIP often occurs within 3 months after ICIs treatment, and the clinical and CT findings are lack of specificity. Radiologic features may have a profound value in predicting the outcome of CIP.

Association of Clinical and Radiological Features with Disease Severity of Symptomatic Immune Checkpoint Inhibitor-Related Pneumonitis.

To investigate the predictive ability of clinical and chest computed tomography (CT) features to predict the severity of symptomatic immune checkpoint inhibitor-related pneumonitis (CIP). This study included 34 patients diagnosed with symptomatic CIP (grades 2-5) and divided into mild (grade 2) and severe CIP (grades 3-5) groups. The groups' clinical and chest CT features were analyzed. Three manual scores (extent, image finding, and clinical symptom scores) were conducted to evaluate the diagnostic performance alone and in combination. There were 20 cases of mild CIP and 14 cases of severe CIP. More severe CIP occurred within 3 months than after 3 months (11 vs. 3 cases, p = 0.038). Severe CIP was significantly associated with fever (p < 0.001) and the acute interstitial pneumonia/acute respiratory distress syndrome pattern (p = 0.001). The diagnostic performance of chest CT scores (extent score and image finding score) was better than that of clinical symptom score. The combination of the three scores demonstrated the best diagnostic value, with an area under the receiver operating characteristic curve of 0.948. The clinical and chest CT features have important application value in assessing the disease severity of symptomatic CIP. We recommend the routine use of chest CT in a comprehensive clinical evaluation.