Reduction in Chest CT Severity and Improved Hospital Outcomes in SARS-CoV-2 Omicron Compared with Delta Variant Infection.

Background The SARS-Cov-2 Omicron variant demonstrates rapid spread but reduced disease severity. Studies evaluating lung imaging findings of Omicron infection versus non-Omicron infection remain lacking. Purpose To compare the Omicron variant with the SARS-CoV-2 Delta variant according to their chest CT radiologic pattern, biochemical parameters, clinical severity, and hospital outcomes after adjusting for vaccination status. Materials and Methods This retrospective study included hospitalized adult patients with reverse transcriptase-polymerase chain reaction test results positive for SARS-CoV-2, with CT pulmonary angiography performed within 7 days of admission between December 1, 2021, and January 14, 2022. Multiple readers performed blinded radiologic analyses that included RSNA CT classification, chest CT severity score (CTSS) (range, 0 [least severe] to 25 [most severe]), and CT imaging features, including bronchial wall thickening. Results A total of 106 patients (Delta group, n = 66; Omicron group, n = 40) were evaluated (overall mean age, 58 years ± 18 [SD]; 58 men). In the Omicron group, 37% of CT pulmonary angiograms (15 of 40 patients) were categorized as normal compared with 15% (10 of 66 patients) of angiograms in the Delta group (P = .016). A generalized linear model was used to control for confounding variables, including vaccination status, and Omicron infection was associated with a CTSS that was 7.2 points lower than that associated with Delta infection (ß = -7.2; 95% CI: -9.9, -4.5; P < .001). Bronchial wall thickening was more common with Omicron infection than with Delta infection (odds ratio [OR], 2.4; 95% CI: 1.01, 5.92; P = .04). A booster shot was associated with a protective effect for chest infection (median CTSS, 5; IQR, 0-11) when compared with unvaccinated individuals (median CTSS, 11; IQR, 7.5-14.0) (P = .03). The Delta variant was associated with a higher OR of severe disease (OR, 4.6; 95% CI: 1.2, 26; P = .01) and admission to a critical care unit (OR, 7.0; 95% CI: 1.5, 66; P = .004) when compared with the Omicron variant. Conclusion The SARS-CoV-2 Omicron variant was associated with fewer and less severe changes on chest CT images compared with the Delta variant. Patients with Omicron infection had greater frequency of bronchial wall thickening but less severe disease and improved hospital outcomes when compared with patients with Delta infection. © RSNA, 2022 Online supplemental material is available for this article.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

Clinical and economic impact of extracardiac lesions on coronary CT angiography.

OBJECTIVE: When reporting coronary CT angiography (CCTA), extracardiac structures are routinely assessed, usually on a wide field-of-view (FOV) reconstruction. We performed a retrospective observational cross-sectional study to investigate the impact of incidental extracardiac abnormalities on resource utilisation and treatment, and cost-effectiveness. METHODS: All patients undergoing CCTA at a single institution between January 2012 and March 2020 were identified. The indication for CCTA was chest pain or dyspnoea in >90%. Patients with ≥1 significant extracardiac findings were selected. Clinical follow-up, investigations and treatment were documented, and costs were calculated. RESULTS: 4340 patients underwent CCTA; 717 extracardiac abnormalities were identified in 687 individuals (15.8%; age 62±12 years; male 336, 49%). The abnormality was already known in 162 (23.6%). Lung nodules and cysts were the most common abnormalities (296, 43.1%). Clinical and/or imaging follow-up was pursued in 292 patients (42.5%). Treatment was required by 14 patients (0.3% of the entire population), including lung resection for adenocarcinoma in six (0.1%). All but two abnormalities (both adenocarcinomas) were identifiable on the limited cardiac FOV. The cost of reporting (£20) and follow-up (£33) of extracardiac abnormalities was £53 per patient. The cost per discounted quality-adjusted life year was £23 930, increasing to £46 674 for reporting the wide FOV rather than the cardiac FOV alone. CONCLUSIONS: Extracardiac abnormalities are common on CCTA, but identification and follow-up are costly. The few requiring treatment are usually identifiable without review of the wide FOV. The way in which CCTAs are scrutinised for extracardiac abnormalities in a resource-limited healthcare system should be questioned.

The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines.

PURPOSE: To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum. METHOD: An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention. RESULTS: The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans <0.56% group (n = 13) could be obviated resulting in an overall reduction of 18.6% (45/242) CT scans in the benign cohort. In the 80% group (n = 10), expedited intervention of malignant nodules could result in a 3.6-month reduction in time delay in 5 cancer patients. CONCLUSION: We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.

Lung cancer prediction by Deep Learning to identify benign lung nodules.

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules.

The role of coronial autopsies in a context of decreasing hospital autopsies: an investigation of the issues.

This article scrutinises the argument that decreasing hospital autopsy rates are outside the control of medical personnel, based as they are on families' unwillingness to consent to autopsy procedures, and that, as a consequence, the coronial autopsy is the appropriate alternative to the important medical and educational role of the autopsy It makes three points which are well supported by the research. First, that while hospital autopsy rates are decreasing, they have been doing so for more than 60 years, and issues beyond the simple notion of consent, like funding formulae in hospitals, increased technology and fear of litigation by doctors are all playing their part in this decline. Secondly, the issue of consent has as much to do with families not being approached as with families declining to give consent. This is well supported by recent changes in hospital policy and procedures which include senior medical personnel and detailed consent forms, both of which have been linked to rising consent rates in recent years. Finally, the perception that coronial autopsies are beyond familial consent has been challenged recently by legislative changes in both Australia and the United States of America which allow objections based on religion and culture to be heard by coroners. For these reasons, it is argued that medical personnel need to focus on increasing hospital autopsy rates, while also addressing the complex ethical issues associated with conducting medical research within the context of the coronial autopsy.

A hidden lung cancer in a patient with granulomatosis with polyangiitis.

Granulomatosis with polyangiitis is a systemic necrotizing vasculitis that affects the small- and medium-sized blood vessels. The diagnosis can be challenging since the clinical and imaging findings have similarities with infection, and malignancy. Serologic and histopathological investigations often help confirm the diagnosis. However, this can be falsely reassuring. We present a unique case of the coexistence of vasculitis and squamous cell carcinoma in the same cavitating lung mass. The case highlights the importance of recognizing changes in disease behaviour early to allow for timely management.

A case series of thoracic dynamic contrast-enhanced MR lymphangiography: technique and applications.

Dynamic contrast-enhanced magnetic resonance lymphangiography is a radiation-free, high spatial resolution technique which is increasingly used to evaluate thoracic lymphatic disorders and for pre-procedural planning. DCE has the added advantage of allowing dynamic real-time evaluation of lymphatic flow. It can be employed to investigate commonly encountered clinical situations such as recurrent pleural effusions following trauma, thoracic duct injury after thoracic surgery, and exclude diseases and congenital malformations of the thoracic lymphatic system. The imaging procedure and protocol are detailed in this case series to highlight the application of dynamic contrast-enhanced magnetic resonance lymphangiography in everyday practice and its importance to guide surgical planning.

COVID-19 pneumonia and the masquerades.

During the COVID-19 pandemic, chest CT is frequently used to help with the diagnosis. The classic CT patterns of COVID-19 pneumonia are well-published and recognised among radiologists. However, when there are pre-existing conditions particularly in the elderly population that could mask or result in similar patterns of disease, then the diagnosis is more difficult. This imaging essay highlights the commonly encountered situations including patients with heart failure, other possible infections particularly in the immunodeficient, and when there is trauma to the thorax. We illustrate imaging clues available to the radiologist to either make the diagnosis or at least reduce the differential diagnosis.

Isolated leptomeningeal carcinomatosis and possible fungal meningitis as late sequelae of oesophageal adenocarcinoma.

We describe a case of a 67-year-old man with known chronic obstructive pulmonary disease, type 2 diabetes mellitus, hypertension, osteoarthritis, previous history of excess alcohol intake, and oesophagectomy 3 years earlier for T3N0 adenocarcinoma, referred by his general practitioner with confusion, weight loss and several recent falls. CT of the chest, abdomen and pelvis revealed a right middle-lobe pulmonary embolism, while CT of the head revealed a communicating hydrocephalus. Lumbar puncture was performed, and empirical treatment for tuberculous and fungal meningitis was commenced. Unfortunately, he suffered a rapid neurological deterioration with markedly elevated cerebrospinal fluid (CSF) pressures, leading to an external ventricular drain. Cytological analysis of a CSF sample revealed a cellular infiltrate consistent with leptomeningeal carcinomatosis (adenocarcinoma), with the previous oesophageal malignancy the likely primary. He passed away 17 days after hospital admission. Prolonged culture of CSF later produced evidence of two distinct phaeomycotic moulds (Cladosporium sp and Exophiala sp), suggesting that fungal meningitis may also have contributed to the clinical picture.

Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol.

INTRODUCTION: Lung cancer is a common cancer, with over 1.3 million cases worldwide each year. Early diagnosis using computed tomography (CT) screening has been shown to reduce mortality but also detect non-malignant nodules that require follow-up scanning or alternative methods of investigation. Practical and accurate tools that can predict the probability that a lung nodule is benign or malignant will help reduce costs and the risk of morbidity and mortality associated with lung cancer. METHODS: Retrospectively collected data from 1500 patients with pulmonary nodule(s) of up to 15 mm detected on routinely performed CT chest scans aged 18 years old or older from three academic centres in the UK will be used to to develop risk stratification models. Radiological, clinical and patient characteristics will be combined in multivariable logistic regression models to predict nodule malignancy. Data from over 1000 participants recruited in a prospective phase of the study will be used to evaluate model performance. Discrimination, calibration and clinical utility measures will be presented.