Novel Applications and Refinements of Ultrasound Techniques in Perinatal and Infant Death Investigation

The decline in parental consent for perinatal autopsies has led to the development of less invasive autopsy techniques, primarily using imaging and in some cases acquiring tissue samples through laparoscopic techniques via small incisions. Whilst post-mortem MRI (PMMR) has been extensively tested, and shown to be a suitable modality for determining the cause of death/demise in the perinatal cohort, it is not widely available. This thesis explores the utility of a poorly explored, yet more accessible, cheaper, dynamic imaging modality widely used in ‘live’ paediatric imaging for the purposes of a less invasive autopsy – the perinatal post-mortem ultrasound (PMUS). I present a systematic review of the limited literature of PMUS diagnostic accuracy for perinatal death investigation encompassing 4 publications with 455 cases in total. This reveals an overall pooled whole body sensitivity rate of 73.3% [95% CI 59.9, 83.5] and specificity rate of 96.6% [95% CI 92.6, 98.4] . A comprehensive imaging protocol for whole body PMUS is also presented based on my own experience in scanning 272 perinatal deaths. An analysis in 130 of these cases with autopsy as a reference standard, showed that brain and abdominal diagnoses yielded the highest sensitivity rates (90.9% and 92.3% respectively), with spinal, cardiac and thoracic diagnoses yielding the lowest sensitivity rates (50%, 50% and 57.1% respectively). Imaging of the brain and heart however were the least likely to be of diagnostic quality (76.8% and 78.3% diagnostic cases respectively), particularly in macerated fetuses. In a subset of cases where PMUS and 1.5T PMMR were performed, there was no significant difference in whole body diagnostic accuracy rates (concordance rates for PMUS versus PMMR of 86.4% [95%CI 77.7, 92.0] versus 88.6% [95% CI 80.3, 93.7]), although PMMR yielded fewer non-diagnostic brain and cardiac examinations (2.9% and 2.9% non-diagnostic brain and cardiac PMMR cases versus 22.8% and 14.7% non-diagnostic PMUS cases). In the second part of my thesis, I describe the development of an ‘incisionless’ ultrasound guided biopsy method using a single entry site for the biopsy needle – the umbilical vein. This ‘INTACT’ biopsy method allowed for a ‘non-invasive’ autopsy with tissue sampling, with a biopsy success rate of 76.1% overall for all organs, with highest individual organ success rates >90% for heart and lungs. I conclude by discussing how best to incorporate PMUS into clinical practice and suggest areas for future researc

Imaging findings of multisystem inflammatory syndrome in children associated with COVID-19

[Background] A hyperinflammatory immune-mediated shock syndrome has been recognised in children exposed to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19).[Objective] To describe typical imaging findings in children with multisystem inflammatory syndrome associated with COVID-19.[Materials and methods] During the first wave of the COVID-19 pandemic, imaging studies and clinical data from children treated for multisystem inflammatory syndrome were collected from multiple centres. Standardised case templates including demographic, biochemical and imaging information were completed by participating centres and reviewed by paediatric radiologists and paediatricians.[Results] We included 37 children (21 boys; median age 8.0 years). Polymerase chain reaction (PCR) testing was positive for SARS-CoV-2 in 15/37 (41%) children and immunoglobulins in 13/19 children (68%). Common clinical presentations were fever (100%), abdominal pain (68%), rash (54%), conjunctivitis (38%) and cough (32%). Thirty-three children (89%) showed laboratory or imaging findings of cardiac involvement. Thirty of the 37 children (81%) required admission to the intensive care unit, with good recovery in all cases. Chest radiographs demonstrated cardiomegaly in 54% and signs of pulmonary venous hypertension/congestion in 73%. The most common chest CT abnormalities were ground-glass and interstitial opacities (83%), airspace consolidation (58%), pleural effusion (58%) and bronchial wall thickening (42%). Echocardiography revealed impaired cardiac function in half of cases (51%) and coronary artery abnormalities in 14%. Cardiac MRI showed myocardial oedema in 58%, pericardial effusion in 42% and decreased left ventricular function in 25%. Twenty children required imaging for abdominal symptoms, the commonest abnormalities being free fluid (71%) and terminal ileum wall thickening (57%). Twelve children underwent brain imaging, showing abnormalities in two cases.[Conclusion] Children with multisystem inflammatory syndrome showed pulmonary, cardiac, abdominal and brain imaging findings, reflecting the multisystem inflammatory disease. Awareness of the imaging features of this disease is important for early diagnosis and treatment.Peer reviewe

Thoracic imaging of coronavirus disease 2019 (COVID-19) in children: a series of 91 cases

BACKGROUND Pulmonary infection with SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2; COVID-19) has rapidly spread worldwide to become a global pandemic. OBJECTIVE To collect paediatric COVID-19 cases worldwide and to summarize both clinical and imaging findings in children who tested positive on polymerase chain reaction testing for SARS-CoV-2. MATERIALS AND METHODS Data were collected by completion of a standardised case report form submitted to the office of the European Society of Paediatric Radiology from March 12 to April 8, 2020. Chest imaging findings in children younger than 18 years old who tested positive on polymerase chain reaction testing for SARS-CoV-2 were included. Representative imaging studies were evaluated by multiple senior paediatric radiologists from this group with expertise in paediatric chest imaging. RESULTS Ninety-one children were included (49 males; median age: 6.1 years, interquartile range: 1.0 to 13.0 years, range: 9 days-17 years). Most had mild symptoms, mostly fever and cough, and one-third had coexisting medical conditions. Eleven percent of children presented with severe symptoms and required intensive unit care. Chest radiographs were available in 89% of patients and 10% of them were normal. Abnormal chest radiographs showed mainly perihilar bronchial wall thickening (58%) and/or airspace consolidation (35%). Computed tomography (CT) scans were available in 26% of cases, with the most common abnormality being ground glass opacities (88%) and/or airspace consolidation (58%). Tree in bud opacities were seen in 6 of 24 CTs (25%). Lung ultrasound and chest magnetic resonance imaging were rarely utilized. CONCLUSION It seems unnecessary to perform chest imaging in children to diagnose COVID-19. Chest radiography can be used in symptomatic children to assess airway infection or pneumonia. CT should be reserved for when there is clinical concern to assess for possible complications, especially in children with coexisting medical conditions

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study.

Peer reviewed: TrueFunder: National Institute for Health and Care Research; FundRef: http://dx.doi.org/10.13039/501100000272OBJECTIVE: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination. DESIGN: Prospective multi-reader diagnostic accuracy study. SETTING: United Kingdom. PARTICIPANTS: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. MAIN OUTCOME MEASURES: Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass). RESULTS: When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs. CONCLUSIONS: When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered "non-interpretable.

Postmortem microfocus computed tomography for early gestation fetuses: a validation study against conventional autopsy

Background: Perinatal autopsy provides useful clinical information in up to 40% of cases. However, there is a substantial unmet clinical need with regards to postmortem investigation of early gestation fetal loss for parents for whom standard autopsy is either not available or not acceptable. Parents dislike the invasive nature of autopsy, but current clinical imaging techniques do not provide high-enough imaging resolution in small fetuses. We hypothesized that microfocus computed tomography, which is a rapid high-resolution imaging technique, could give accurate diagnostic imaging after early gestation fetal loss. Objective: The objective of the study was to evaluate the diagnostic accuracy of microfocus computed tomography for noninvasive human fetal autopsy for early gestation fetuses, with the use of conventional autopsy as the reference standard. Study Design: We compared iodinated whole body microfocus computed tomography in 20 prospectively recruited fetuses (11–21 weeks gestation from 2 centers) with conventional autopsy in a double-blinded manner for a main diagnosis and findings in specific body organs. Fetuses were prepared with 10% formalin/potassium tri-iodide. Images were acquired with a microfocus computed tomography scanner with size-appropriate parameters. Images were evaluated independently by 2 pediatric radiologists, who were blinded to formal perinatal autopsy results, across 40 individual indices to reach consensus. The primary outcome was agreement between microfocus computed tomography and conventional autopsy for overall diagnosis. Results: Postmortem whole body fetal microfocus computed tomography gave noninvasive autopsy in minutes, at a mean resolution of 27μm, with high diagnostic accuracy in fetuses at 14 weeks gestation (agreement, 97.2% and 97.9%, respectively). Within first-trimester fetal loss cases (<14 weeks gestation), microfocus computed tomography analysis yielded significantly fewer nondiagnostic indices than autopsy examination (22/440 vs 48/348, respectively; P<.001). Conclusion: Postmortem whole-body fetal microfocus computed tomography gives noninvasive, detailed anatomic examinations that are achieved in minutes at high resolution. Microfocus computed tomography may be preferable to magnetic resonance imaging in early gestation fetuses and may offer an acceptable method of examination after fetal loss for parents who decline invasive autopsy. This will facilitate autopsy and subsequent discussions between medical professionals who are involved in patient care and counselling for future pregnancies.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Novel usage of microfocus computed tomography (micro-CT) for visualisation of human embryonic development—Implications for future non-invasive post-mortem investigation

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Imaging findings of multisystem inflammatory syndrome in children associated with COVID-19

BACKGROUND A hyperinflammatory immune-mediated shock syndrome has been recognised in children exposed to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). OBJECTIVE To describe typical imaging findings in children with multisystem inflammatory syndrome associated with COVID-19. MATERIALS AND METHODS During the first wave of the COVID-19 pandemic, imaging studies and clinical data from children treated for multisystem inflammatory syndrome were collected from multiple centres. Standardised case templates including demographic, biochemical and imaging information were completed by participating centres and reviewed by paediatric radiologists and paediatricians. RESULTS We included 37 children (21 boys; median age 8.0 years). Polymerase chain reaction (PCR) testing was positive for SARS-CoV-2 in 15/37 (41%) children and immunoglobulins in 13/19 children (68%). Common clinical presentations were fever (100%), abdominal pain (68%), rash (54%), conjunctivitis (38%) and cough (32%). Thirty-three children (89%) showed laboratory or imaging findings of cardiac involvement. Thirty of the 37 children (81%) required admission to the intensive care unit, with good recovery in all cases. Chest radiographs demonstrated cardiomegaly in 54% and signs of pulmonary venous hypertension/congestion in 73%. The most common chest CT abnormalities were ground-glass and interstitial opacities (83%), airspace consolidation (58%), pleural effusion (58%) and bronchial wall thickening (42%). Echocardiography revealed impaired cardiac function in half of cases (51%) and coronary artery abnormalities in 14%. Cardiac MRI showed myocardial oedema in 58%, pericardial effusion in 42% and decreased left ventricular function in 25%. Twenty children required imaging for abdominal symptoms, the commonest abnormalities being free fluid (71%) and terminal ileum wall thickening (57%). Twelve children underwent brain imaging, showing abnormalities in two cases. CONCLUSION Children with multisystem inflammatory syndrome showed pulmonary, cardiac, abdominal and brain imaging findings, reflecting the multisystem inflammatory disease. Awareness of the imaging features of this disease is important for early diagnosis and treatment

Discrete Choice Experiment on a Magnetic Resonance Imaging Scoring System for Temporomandibular Joints in Juvenile Idiopathic Arthritis

Objective To determine the relative importance weights of items and grades of a newly developed additive outcome measure called the juvenile idiopathic arthritis (JIA) magnetic resonance imaging (MRI) scoring system for the temporomandibular joint (TMJ) (JAMRIS-TMJ). Methods An adaptive partial-profile, discrete choice experiment (DCE) survey using the 1000Minds platform was independently completed by members of an expert group consisting of radiologists and non-radiologist clinicians to determine the group-averaged relative weights for the JAMRIS-TMJ. Subsequently, an image-based vignette ranking exercise was done, during which experts individually rank ordered 14 patient vignettes for disease severity while blinded to the weights and unrestricted to JAMRIS-TMJ assessment criteria. Validity of the weighted JAMRIS-TMJ was tested by comparing the consensus-graded, DCE-weighted JAMRIS-TMJ score of the vignettes with their unrestricted image-based ranks provided by the experts. Results Nineteen experts completed the DCE survey, and 21 completed the vignette ranking exercise. Synovial thickening and joint enhancement showed higher weights per raw score compared to bone marrow items and effusion in the inflammatory domain, while erosions and condylar flattening showed nonlinear and higher weights compared to disk abnormalities in the damage domain. The weighted JAMRIS-TMJ score of the vignettes correlated highly with the ranks from the unrestricted comparison method, with median Spearman's ρ of 0.92 (interquartile range [IQR] 0.87–0.95) for the inflammation and 0.93 (IQR 0.90–0.94) for the damage domain. Conclusion A DCE survey was used to quantify the importance weights of the items and grades of the JAMRIS-TMJ. The weighted score showed high convergent validity with an unrestricted, holistic vignette ranking method

Thoracic imaging of coronavirus disease 2019 (COVID-19) in children: a series of 91 cases

Background Pulmonary infection with SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2; COVID-19) has rapidly spread worldwide to become a global pandemic. Objective To collect paediatric COVID-19 cases worldwide and to summarize both clinical and imaging findings in children who tested positive on polymerase chain reaction testing for SARS-CoV-2. Materials and methods Data were collected by completion of a standardised case report form submitted to the office of the European Society of Paediatric Radiology from March 12 to April 8, 2020. Chest imaging findings in children younger than 18 years old who tested positive on polymerase chain reaction testing for SARS-CoV-2 were included. Representative imaging studies were evaluated by multiple senior paediatric radiologists from this group with expertise in paediatric chest imaging. Results Ninety-one children were included (49 males; median age: 6.1 years, interquartile range: 1.0 to 13.0 years, range: 9 days-17 years). Most had mild symptoms, mostly fever and cough, and one-third had coexisting medical conditions. Eleven percent of children presented with severe symptoms and required intensive unit care. Chest radiographs were available in 89% of patients and 10% of them were normal. Abnormal chest radiographs showed mainly perihilar bronchial wall thickening (58%) and/or airspace consolidation (35%). Computed tomography (CT) scans were available in 26% of cases, with the most common abnormality being ground glass opacities (88%) and/or airspace consolidation (58%). Tree in bud opacities were seen in 6 of 24 CTs (25%). Lung ultrasound and chest magnetic resonance imaging were rarely utilized. Conclusion It seems unnecessary to perform chest imaging in children to diagnose COVID-19. Chest radiography can be used in symptomatic children to assess airway infection or pneumonia. CT should be reserved for when there is clinical concern to assess for possible complications, especially in children with coexisting medical conditions

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study

Peer reviewed: TrueFunder: National Institute for Health and Care Research; FundRef: http://dx.doi.org/10.13039/501100000272Objective: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination. Design: Prospective multi-reader diagnostic accuracy study. Setting: United Kingdom. Participants: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. Main outcome measures: Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass). Results: When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs. Conclusions: When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered “non-interpretable.