Retrospective analysis of 1118 outpatient chest CT scans to determine factors associated with excess scan length

Abstract Introduction Emerging data suggests that SARS-CoV-2 acts via the angiotensin-converting-enzyme-2 (ACE2) receptors in the cells, also present in the gastrointestinal and liver cells, resulting in gastrointestinal manifestations of COVID-19. It is challenging to diagnose COVID-19 in patients presenting with only gastrointestinal symptoms, with early suspicion and visualisation of the lung bases on computerised tomography(CT) abdomen scans being helpful. Methods Single-centre retrospective analysis of abdominal/chest CT scans was conducted within a District General Hospital. From the 6000 CT scans between March and April 2020, 1557(26%) were abdominal, 230 of which had chest CT scans with COVID-19 positive changes. 49 patients with CT abdomen and a positive CT Chest were shortlisted. All recorded documentation was assessed using online clinical portal and tabulated using Microsoft Excel®. Subdivision into 3 groups was: 1) primary gastrointestinal symptom, normal CT abdomen, 2) common COVID-19 symptoms with gastrointestinal symptoms, normal CT abdomen 3) gastrointestinal symptoms, abdominal pathology on radiology. Results COVID-19 related changes were seen on CT scans with no abdominal pathology noted in 33% (n = 16) patients with primary gastrointestinal symptoms. Further 18% (9 patients) presented with gastrointestinal symptoms alongside other common COVID-19 symptoms, however abdominal CT scan was normal. CT abdomen of these 25 patients were studied by a consultant radiologist, who examined the lung bases on the abdominal CT scan for COVID-19 related changes, which was seen in 92% patients. In 8% of the cases where the lung bases on the CT abdomen were normal, COVID-19 related changes were apparent on the CT chest. Conclusions CT scans of the abdomen should be supplemented with CT scans of the chest, when appropriate, for early accurate diagnosis, early treatment and triage to the correct wards, especially at the height of the pandemic.

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The Role of Computed Tomography in Differentiation of Coronavirus Pneumonia, its Complications and Comorbidities. Case Reports

The Russian Archives of Internal Medicine ◽

10.20514/2226-6704-2020-10-5-357-371 ◽

2020 ◽

Vol 10 (5) ◽

pp. 357-371

Author(s):

V. S. Petrovichev ◽

A. V. Melekhov ◽

M. A. Sayfullin ◽

I. G. Nikitin

Keyword(s):

Computed Tomography ◽

Retrospective Analysis ◽

Computer Tomography ◽

Bacterial Pneumonia ◽

Case Reports ◽

Chest Ct ◽

Pulmonary Diseases ◽

Ct Scans ◽

Ct Features

Background: computer tomography (CT) features of COVID-19, their temporal changes and differences from other pulmonary (viral and bacterial pneumonia) and non-pulmonary diseases are well described in recent publications. The prevalence and characteristics of signs of concomitant problems that could be identified at chest CT are less studied.Aim: to analyze the prevalence and characteristics of chest CT features of COVID-19, its complications and comorbidities.Methods: retrospective analysis of CT and clinical data of 354 patients hospitalized with suspected COVID at April and May of 2020.Results: 962 CT scans were analyzed (3 (2-3) scans per patient). First CT was performed at 8 (5-11) day of sickness. Several roentgenological scenarios could be highlighted: patients with coronavirus pneumonia (n=295; 83%); with combination of COVID-19 and another pathology (n=36; 10%); with complications of COVID-19 (n=12; 3%); with alternative pathology (n=2; 1%); without any pathological signs (n=9; 3%). Several cases, illustrating CT signs of coronavirus pneumonia, its complications and comorbidities are reported.Conclusion: CT possibilities are not limited to detect typical COVID-19 signs, it also helps to differentiate pulmonary and other thoracis pathology.

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Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) ◽

10.1109/caida51941.2021.9425269 ◽

2021 ◽

Author(s):

Amjad Rehman ◽

Muhammad Kashif ◽

Ibrahim Abunadi ◽

Noor Ayesha

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Cancer Detection ◽

Chest Ct ◽

Machine Learning Techniques ◽

Ct Scans ◽

Learning Techniques ◽

Lung Cancer Detection

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Factors associated with infrainguinal bypass graft patency at 1-year; a retrospective analysis of a single centre experience

Perfusion ◽

10.1177/0267659121995760 ◽

2021 ◽

pp. 026765912199576

Author(s):

Pasha Normahani ◽

Ismail Yusuf Anwar ◽

Alona Courtney ◽

Amish Acharya ◽

Viknesh Sounderajah ◽

...

Keyword(s):

Retrospective Analysis ◽

Patency Rate ◽

Bypass Graft ◽

Graft Patency ◽

Primary Patency ◽

Rank Test ◽

Secondary Patency ◽

Factors Associated ◽

Independent Variables ◽

Secondary Patency Rate

Introduction: The aim of this study was to identify factors associated with primary graft patency 1 year following open lower limb revascularisation (LLR) at a tertiary referral vascular service. Methods: A retrospective analysis of patients undergoing infra-inguinal bypass surgery between January 2016 and May 2017 at a tertiary vascular centre (St Mary’s Hospital, London) was performed. Data regarding patient demographics, comorbidities, type of operation and post-operative anti-thrombotic strategy were collected. Quality of run-off score was assessed from pre-operative imaging. Results: Seventy-seven cases were included in the analysis. Overall, the primary patency rate at 1-year was 63.6% ( n = 49/77) and the secondary patency rate was 67.5% ( n = 52/77). Independent variables with statistically significant inferior patency rates at 1-year were (1) bypasses with below knee targets (p = 0.0096), (2) chronic limb threatening ischaemia indication (p = 0.038), (3) previous ipsilateral revascularisation (p < 0.001) and (4) absence of hypertension history (p = 0.041). There was also a trend towards significance for American Society of Anesthesiologists (ASA) grade (p = 0.06). Independent variables with log-rank test p values of <0.1 were included in a Cox proportional hazards model. The only variable with a statistically significant impact on primary patency rates was previous open or endovascular ipsilateral revascularisation (HR 2.44 (1.04–5.7), p = 0.04). Conclusion: At 1-year follow-up, previous ipsilateral revascularisation was the most significant factor in affecting patency rates. Patients in this subgroup should therefore be deemed high-risk, which should be reflected in the informed consent and peri-operative management.

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Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414214 ◽

2021 ◽

Author(s):

Shahin Heidarian ◽

Parnian Afshar ◽

Arash Mohammadi ◽

Moezedin Javad Rafiee MD ◽

Anastasia Oikonomou MD ◽

...

Keyword(s):

Feature Extraction ◽

Chest Ct ◽

Ct Scans ◽

Disease Identification

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Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

La radiologia medica ◽

10.1007/s11547-021-01390-4 ◽

2021 ◽

Author(s):

Martina Pecoraro ◽

Stefano Cipollari ◽

Livia Marchitelli ◽

Emanuele Messina ◽

Maurizio Del Monte ◽

...

Keyword(s):

Chest Ct ◽

Ct Scans ◽

Imaging Biomarker ◽

Cross Sectional ◽

Mri Scans ◽

Magnetic Resonance Imaging Mri ◽

Sectional Analysis ◽

Ct And Mri ◽

Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

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