Quantified CT Evaluation in coronavirus disease (COVID-19): A study of 30 Patients in Chongqing, China

Abstract Background Chest computed tomography (CT) provides insight into the progression and prognosis of COVID-19 pneumonia. Purpose To quantify the chest CT scans of patients with CODIV-19 pneumonia using the pulmonary inflammation index (PII)and associate it with the severity of pneumonia. Methods A total of thirty inpatients admitted between January 30 and February 29, 2020 with confirmed COVID-19 infection were enrolled in this retrospective review. Patients were classified as “severe”(those who met the severe pneumonia criteria) or “mild”. Chest CT scans and clinical statistics data were obtained at four milestones (the date of admission, 3 days after treatment, 1 week after treatment and the time the last CT scan was obtained before discharge orthe completionof our research). Results Thirty patients (18 males and 12 females, age 20–74 years) with confirmed COVID-19pneumonia were evaluated. Increased neutrophilswere noted in 11 (36.7%) patients and decreased in 3 (10%) patients. Elevation of C-reactive protein (CRP) in 22 (73.3%) patients and erythrocyte sedimentation rate in 27 (90%) patient were observed, but elevation of procalcitonin was not obvious. Seven (53.8%) patients had elevation of lactate dehydrogenase (LDH).The presentation of CT opacities was mainly in the form of distribution in both the severe andmild groups. The mean PII score in the severe group was 58% and 13.7% in the mild group. The score in the severe group was more than 50%and less than 20%in the mild group at every milestone. The score in the severe group was always higher than the mild group, therefore, the severity of the disease may be positively correlated with PII score. Conclusion The pulmonary inflammation index (PII) score of chest CT scans correlated with coronavirus disease (COVID-19) progression and could be used to indicate severity in patients.

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Abstract P105: Identification of Cardioembolic Sources and Myocardial Injury in Acute Ischemic Stroke With Delayed-Phase, Low-Dose Chest Ct by Means of a Dual-Layer Spectral Ct Scanner

Stroke ◽

10.1161/str.52.suppl_1.p105 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Gaston Rodriguez Granillo ◽

Juan José Cirio ◽

Ivan Lylyk ◽

Nicolas Perez ◽

Maria L Caballero ◽

...

Keyword(s):

Myocardial Injury ◽

Low Dose ◽

Late Enhancement ◽

Chest Ct ◽

Ct Scans ◽

Ct Scanner ◽

Spectral Ct ◽

Thrombotic Complications ◽

The Mean ◽

Delayed Phase

Background: The COVID-19 pandemic has promoted adaptations in diagnostic algorithms. We explored the feasibility and accuracy of delayed phase (DP) chest computed tomography (CT) performed immediately after brain CT perfusion (CTP) for the identification of thrombotic complications and myocardial fibrosis among patients admitted with acute ischemic stroke (AIS). Methods: Since July, we have incorporated the use of low dose chest CT scans using a spectral CT scanner in all patients admitted with AIS, encouraging acquisitions, five min after brain CTP. All scans were non gated and comprised low dose chest CT scans, without additional contrast. Using virtual monochromatic imaging and iodine maps, we evaluated the presence of thrombotic complications, myocardial late enhancement, and myocardial extracellular volume (ECV), as a surrogate of edema and interstitial fibrosis. Results: We included 22 patients. The mean age was 66.2±19.6 years. In 5 patients, a cardioembolic (CE) source was later identified by transesophageal echocardiogram (TEE), [left atrial appendage (LAA) thrombus, n=1], transthoracic echocardiogram with agitated saline injection (patent foramen ovale n=2), or by EKG (atrial fibrillation). Seven patients further underwent either TEE or cardiac CT to identify CE sources. DP non gated chest CT had a sensitivity and specificity of 100% to identify CE sources, 1 LAA thrombus correctly detected. Chest CT identified pulmonary thromboembolism (PE), later confirmed with CT angiography. Chest CT identified myocardial late enhancement in 16 patients (80% in CE vs. 71% in non CE, p=0.68), myocardial fat in 1, and coronary calcification in 77% [with 2.6±2.2 vs 3.8±3.6 coronary calcified segments in CE vs. non CE strokes, p=0.36). The mean ECV was 35±4% in CE vs 32±6% in non CE strokes (p=0.17). The 2 patients with a positive PCR test for COVID-19 showed evidence of myocardial late iodine enhancement, and incremented ECV of the septal wall (38% and 40%, respectively). Conclusions: In this pilot study, DP, non ECG gated, low dose chest CT scan performed 5 min after brain CTP with a spectral scanner; enabled straightforward identification of CE sources among patients with AIS. This approach allowed detection of PE and myocardial injury.

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Postoperative Computed Tomographic Evaluation of Patients with Large Pituitary Tumors Treated with Operative Decompression and Radiation Therapy

Neurosurgery ◽

10.1227/00006123-199102000-00010 ◽

1991 ◽

Vol 28 (2) ◽

pp. 238-241 ◽

Cited By ~ 1

Author(s):

Paul B. Nelson ◽

Alan G. Robinson ◽

William Hirsch

Keyword(s):

Radiation Therapy ◽

Pituitary Tumors ◽

Residual Tumor ◽

Early Postoperative Period ◽

Ct Scans ◽

Computed Tomographic ◽

Ct Evaluation ◽

Serial Ct ◽

The Mean ◽

Operative Decompression

Abstract Thirty consecutive patients who underwent operative decompression and radiation therapy for large sellar and suprasellar pituitary tumors (⩾2 cm) were studied in terms of the serial computed tomographic (CT) changes. There were 23 men and 7 women. The mean age was 49.6 ± 2.5 years, and the mean follow-up was 45.3 ± 3.9 months. Twenty-eight of the 30 patients had transsphenoidal surgery, and 27 had hormonally inactive tumors. Radiation therapy was begun within 1 month of surgery with a mean dose of 4855 ± 70 cGy. Postoperative CT scans were obtained within 1 month of surgery and at 6- to 12-month intervals thereafter. Fourteen patients (45%) had no suprasellar tumor visualized in either the early postoperative CT scans or on subsequent scans. Eleven patients (35%) had a persistent suprasellar mass during the early postoperative period that resolved on serial CT evaluation. The mean time for resolution was 10.4 ± 1.2 months. Six patients (20%) had a persistent suprasellar mass on serial CT evaluation. A persistent postoperative mass that subsequently resolved in many of the patients was thought to be caused by the gradual retraction of the postoperative packing and hematoma, as well as the effect of radiation on any residual tumor.

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The incidence and outcome of incidental breast lesions detected by computed tomography

Annals of The Royal College of Surgeons of England ◽

10.1308/003588410x12518836439083 ◽

2010 ◽

Vol 92 (2) ◽

pp. 124-126 ◽

Cited By ~ 16

Author(s):

A Hussain ◽

A Gordon-Dixon ◽

H Almusawy ◽

P Sinha ◽

A Desai

Keyword(s):

Breast Cancer ◽

Computed Tomography ◽

Breast Lesion ◽

Chest Ct ◽

Ct Scans ◽

Breast Lesions ◽

Breast Cancers ◽

The Mean ◽

The Uk ◽

Symptomatic Breast

INTRODUCTION In the UK, the majority of breast cancers are diagnosed through symptomatic breast clinics and the breast screening programmes. With increased use of computed tomography (CT) to assess various pathologies, breast lesions are picked up incidentally. The aim of this study was to investigate the incidence and outcomes of breast lesions detected incidentally on CT scans. PATIENTS AND METHODS A retrospective study was conducted to assess the incidence and outcome of incidentally found breast lesions, which were detected on chest CT scans that were conducted for other pathologies during the period from February 2007 to October 2008. RESULTS A total of 432 chest CT scans were performed over 18 months. Thirty-three (7.63%) patients were found to have an incidental breast lesion. The mean age was 73 years (range, 50–86 years). Of these, 17 (52%) were benign, eight (24%) were primary breast cancer and the remaining eight (24%) had no definite pathology. The detection rate of breast cancer was 1.85%. CONCLUSIONS CT is emerging as an important contributor to the detection of occult breast lesions. Radiological awareness of incidental breast lesions is important so that appropriate referral to a specialised breast unit is made.

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Clinical and CT scan findings in patients with COVID-19 pneumonia: A comparison based on disease severity

10.21203/rs.3.rs-476408/v1 ◽

2021 ◽

Author(s):

Sonia Hesam-Shariati ◽

Susan Mohammadi ◽

Morteza Abouzaripour ◽

Behzad Mohsenpour ◽

Bushra Zareie ◽

...

Keyword(s):

Ct Scan ◽

Disease Severity ◽

Clinical Laboratory ◽

Clinical Symptoms ◽

White Blood Cells ◽

Severe Pneumonia ◽

Duration Of Hospitalization ◽

The Mean ◽

Severity Of The Disease ◽

Interlobular Septal Thickening

Abstract Background The SARS-CoV-2 can cause severe pneumonia and highly impact general health. We aimed to investigate different clinical features and CT scan findings of patients with COVID-19 based on disease severity to have a better understanding of this disease. Methods 90 patients with coronavirus were divided into three categories based on the severity of the disease: mild/moderate, severe, and very severe. Clinical, laboratory and CT scan findings of the patients were examined retrospectively. Any association between these features and disease severity were assessed. Results The mean age and duration of hospitalization of patients increased with increasing the severity of disease. The most common clinical symptoms were shortness of breath, cough, and fever. As the severity of the disease increased from mild/moderate to very severe, there was an increase in neutrophile counts and a decrease in lymphocytes and white blood cells (WBC) showing excessive inflammation associated with severe forms of COVID-19. Subpleural changes (81%) and ground-glass opacification/opacity (GGO) lesions (73%) of the lung were the most common features among CT images of COVID-19 patients, and interlobular septal thickening (10%) was the lowest CT feature among patients. Regarding the affected parts of the lung in COVID-19 patients, bilaterial, peripheral and multiple lesions had the highest prevalence. Conclusions It has been shown that clinical, laboratory and CT scan findings varied in COVID-19 patients based on disease severity, which need to be considered carefully in timely diagnosis and treatment of this illness.

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PET/CT Scan as a Surrogate for Treatment Outcomes in Pulmonary Sarcoidosis

10.21203/rs.3.rs-39748/v1 ◽

2020 ◽

Author(s):

Nidah Shabbir Khakoo ◽

Safayeth Jabeen Isma ◽

Mehdi Mirsaeidi

Keyword(s):

Treatment Outcomes ◽

Pulmonary Sarcoidosis ◽

Ct Scans ◽

Serum Angiotensin Converting Enzyme ◽

Reactive Protein ◽

Multiple Organs ◽

Pet Ct ◽

The Mean ◽

One Year ◽

The University

Abstract Background: One of the challenges in treating sarcoidosis is that there is currently no reliable modality to measure disease activity and prognosis. This study was conducted to determine if PET/CT scans could be used to evaluate clinical response to sarcoidosis treatment. Methods: In a retrospective cohort study, subjects with symptomatic pulmonary sarcoidosis followed by the University of Miami Sarcoidosis program from 2015 through 2018 were assessed. Inclusion criteria were subjects ≥18 years who had histologically-confirmed pulmonary sarcoidosis for ≥2 years. Subjects that had PET/CT scans completed prior to starting treatment and approximately one year later were enrolled. Demographics, circulatory blood biomarkers, subjective symptoms, and medications were recorded at baseline (T0) and a year later (T1). Results: Ten subjects with symptomatic pulmonary sarcoidosis were enrolled. All subjects had at least one organ affected by sarcoidosis and a highest of five organs involved (mean 2.75, SD 1.21). Mean serum angiotensin-converting enzyme (ACE) level was 56.0 U/L (SD 43.08), mean lysozyme was 7.78 µg/mL (SD 3.78), and mean C-reactive protein (CRP) was 0.73 mg/dL (SD 0.85) at T0. Further, mean number of positive lesions was 4.27 (SD 2.65) and mean highest SUV was 5.59 (SD 3.59), with a highest of 14.5 at T0. After one year of therapy, a significant improvement in measured outcomes was noted. Dyspnea was absent in all subjects at T1 and only 1 (10%) reported cough at T1. The mean ACE was 42.17 U/L (SD 20.24), mean lysozyme was 6.24 µg/mL (SD 0.80), and mean CRP was 1.67 mg/dL (SD 1.60) at T1. Furthermore, five (50%) subjects were receiving a reduced dose of their respective medication at T1. In terms of PET/CT findings, the mean number of positive lesions decreased at T1 to 1.73 (SD 2.87) and the mean highest SUV decreased to 2.18 (SD 3.3). Conclusion: PET/CT scans can be used as a surrogate modality to help guide treatments in those subjects affected by sarcoidosis, as shown by significant reductions in the involvement of multiple organs. Further studies with larger sample sizes are necessary to explore the potential PET/CT scans have to influence treatment outcomes in sarcoidosis.

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Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

10.1101/2020.10.23.20218461 ◽

2020 ◽

Cited By ~ 3

Author(s):

Aram Ter-Sarkisov

Keyword(s):

Ground Glass Opacity ◽

Chest Ct ◽

Training Data ◽

Classification Model ◽

Ct Scans ◽

Average Precision ◽

True Negative ◽

True Negative Rate ◽

Ct Test ◽

The Mean

In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19. We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately. The best model achieves the mean average precision of 44.68% using MS COCO criterion for instance segmentation across all accuracy thresholds. The classification model, COVID-CT-Mask-Net, which learns to predict the presence of COVID-19 vs common pneumonia vs control, achieves the 93.88% COVID-19 sensitivity, 95.64% overall accuracy, 95.06% common pneumonia sensitivity and 96.91% true negative rate on the COVIDx-CT test split (21192 CT scans) using a small fraction of the training data. We also analyze the effect of Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.

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A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

10.21203/rs.3.rs-38083/v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Feng Pan ◽

Lin Li ◽

Bo Liu ◽

Tianhe Ye ◽

Lingli Li ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Quantitative Computed Tomography ◽

Pulmonary Involvement ◽

Chest Ct ◽

Estimation Methods ◽

Ct Scans ◽

Severe Group ◽

Conventional Ct ◽

Spearman’S Correlation

Abstract Objectives: This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. Materials and Methods: 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: 1. Correlation between these two estimations; 2. Exploring the dynamic patterns using these two estimations between moderate and severe groups.Results: The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p<0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. Conclusions: The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

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The Natural Course of Diffuse Idiopathic Skeletal Hyperostosis in the Thoracic Spine of Adult Males

The Journal of Rheumatology ◽

10.3899/jrheum.171091 ◽

2018 ◽

Vol 45 (8) ◽

pp. 1116-1123 ◽

Cited By ~ 8

Author(s):

Jonneke S. Kuperus ◽

Constantinus F. Buckens ◽

Jurica Šprem ◽

F. Cumhur Oner ◽

Pim A. de Jong ◽

...

Keyword(s):

Computed Tomography ◽

Diffuse Idiopathic Skeletal Hyperostosis ◽

Chest Ct ◽

Ct Scans ◽

Adult Males ◽

Forming Process ◽

Bone Bridge ◽

Elderly Males ◽

The Mean ◽

The Right

Objective.Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by flowing bony bridges on the right side of the spine. Knowledge of the development of these spinal bridges is limited. The current longitudinal computed tomography (CT) study was designed to bridge this gap.Methods.Chest CT scans from elderly males with 2 scans (interval ≥ 2.5 yrs) were retrospectively included. Using the Resnick criteria, a pre-DISH group and a definite DISH group were identified. A scoring system based on the completeness of a bone bridge (score 0–3), extent of fluency, and location of the new bone was created to evaluate the progression of bone formation.Results.In total, 145 of 1367 subjects were allocated to the DISH groups with a mean followup period of 5 years. Overall prevalence of a complete bone bridge increased in the pre-DISH group (11.3% to 31.0%) and in the definite DISH group (45.0% to 55.8%). The mean bridge score increased significantly in both the pre-DISH and definite DISH group (p < 0.001). The new bone gradually became more flowing and expanded circumferentially.Conclusion.Over the mean course of 5 years, the new bone developed from incomplete, pointy bone bridges to more flowing complete bridges. This suggests an ongoing and measurable bone-forming process that continues to progress, also in established cases of DISH.

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Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

Diagnostics ◽

10.3390/diagnostics11050738 ◽

2021 ◽

Vol 11 (5) ◽

pp. 738

Author(s):

Andrej Romanov ◽

Michael Bach ◽

Shan Yang ◽

Fabian C. Franzeck ◽

Gregor Sommer ◽

...

Keyword(s):

Deep Learning ◽

Influenza A ◽

Chest Ct ◽

Viral Pneumonia ◽

Imaging Biomarkers ◽

High Attenuation ◽

Reactive Protein ◽

Radiology Reports ◽

Ct Evaluation ◽

Density Analysis

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600–0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600–0 HU] (r = 0.56, 95% CI = 0.46–0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

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Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

10.21203/rs.3.rs-108547/v1 ◽

2020 ◽

Author(s):

Aram Ter-Sarkisov

Keyword(s):

Ground Glass Opacity ◽

Chest Ct ◽

Training Data ◽

Classification Model ◽

Ct Scans ◽

Average Precision ◽

True Negative ◽

True Negative Rate ◽

Ct Test ◽

The Mean

Abstract In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19. We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately. The best model achieves the mean average precision of 44.68% using MS COCO criterion for instance segmentation across all accuracy thresholds. The classification model, COVID-CT-Mask-Net, which learns to predict the presence of COVID-19 vs common pneumonia vs control, achieves the 93.88% COVID-19 sensitivity, 95.64% overall accuracy, 95.06% common pneumonia sensitivity and 96.91% true negative rate on the COVIDx-CT test split (21192 CT scans) using a small fraction of the training data. We also analyze the effect of Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.

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