My future and I:cardiovascular risk stratification of asymptomatic individuals

In the coming decades, a continuing increase in the number of cases of coronary heart disease (CHD) is expected. This is caused by, amongst others, the increasing prevalence of obesity and diabetes, and the rising numbers of elderly citizens. The morbidity and mortality toll of CHD is high. In many cases, a coronary event occurs acutely, without earlier signs suggesting CHD. So how can we identify individuals in the asymptomatic population at high risk of CHD, and prevent coronary events? Cardiovas- cular risk estimation in the general population is based on determining risk factors such as hypertension and smoking. Risk factor levels can be used to calculate a risk-scoring algorithm, like the European SCORE, and guide medical therapy. Unfortunately, risk factor based algorithms are neither highly sensitive nor specific. Accurate identification of asymptomatic indi- viduals who will develop a coronary event is challenging

Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

Latest CT technologies in lung cancer screening:protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in postprocessing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols