Dataset Information

SARS-CoV-2 infection presenting as colitis with chest and abdomen CT findings.

ABSTRACT: Most of reported symptoms of SARS-CoV-2 infection are related to the respiratory system. Extra pulmonary manifestations of this novel virus infection are being increasingly reported in the literature, with increased attention on the gastrointestinal symptoms which might be the only presenting symptoms in some patients. These GI symptoms are nonspecific and little reported cases in the literature of confirmed gastrointestinal manifestations of SARS-CoV-2 infection by imaging. Colitis related to SARS-CoV-2 is even less reported in the literature. We present a case of SARS-CoV-2 infection of a 40-year-old lady who presented with GI manifestations and features of colitis of the caecum and ascending colon on CT scan. The patient did not have respiratory symptoms but had incidental lung changes in the visualized lung bases. These features were completely resolved as evident clinically and on follow-up CT scan after only 2 weeks, with only supportive care for SARS-CoV-2 infection. GI symptoms, in general, are very common presenting complain for many patients visiting the emergency department; hence, early recognition and high index of clinical suspicion for SARS-CoV-2 infection with the presence of supporting laboratory and imaging findings are to be considered for early protective measures to be undertaken to help in reducing the spread of this virus; in particular, in the middle of global pandemic of this virus and the fact that GI symptoms could be the only presenting symptoms without any respiratory symptoms. More studies and further invasive investigations in patients with features of colitis in imaging are needed to further understand the pathogenesis and its relation to SARS-CoV-2 infection.

SUBMITTER: Khader M

PROVIDER: S-EPMC7505553 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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SARS-CoV-2 infection presenting as colitis with chest and abdomen CT findings.

Khader Mohammed M Al Bishawi Ahmed A Kambal Aalaa A Abdelmajid Alaaeldin A

Radiology case reports 20200921 11

Most of reported symptoms of SARS-CoV-2 infection are related to the respiratory system. Extra pulmonary manifestations of this novel virus infection are being increasingly reported in the literature, with increased attention on the gastrointestinal symptoms which might be the only presenting symptoms in some patients. These GI symptoms are nonspecific and little reported cases in the literature of confirmed gastrointestinal manifestations of SARS-CoV-2 infection by imaging. Colitis related to S ...[more]

PMID: 32983309

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Project description:PurposeOrgan autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging.Acquisition and validation methodsThe dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise.Data format and usage notesThe data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2.Potential applicationsThis dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.

Project description:BackgroundEfforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.MethodsThis retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1-18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.ResultsDLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture.ConclusionCompared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

Dataset Information

SARS-CoV-2 infection presenting as colitis with chest and abdomen CT findings.

Publications

SARS-CoV-2 infection presenting as colitis with chest and abdomen CT findings.

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