Several medical associations recommended lung cancer screening by low-dose computed tomography scanning for high-risk groups. Counselling of the candidates on the potential harms and benefits and their lung cancer risk is a prerequisite for screening.In the NELSON trial, screenings are considered positive for (part) solid lung nodules with a volume >500 mm³and for (part) solid or nonsolid nodules with a volume-doubling time <400 days. For this study, the performance of the NELSON strategy in three screening rounds was evaluated and risk calculations were made for a follow-up period of 5.5 years.458 (6%) of the 7582 participants screened had a positive screen result and 200 (2.6%) were diagnosed with lung cancer. The positive screenings had a predictive value of 40.6% and only 1.2% of all scan results were false-positive. In a period of 5.5 years, the risk of screen-detected lung cancer strongly depends on the result of the first scan: 1.0% after a negative baseline result, 5.7% after an indeterminate baseline and 48.3% after a positive baseline.The screening strategy yielded few positive and false-positive scans with a reasonable positive predictive value. The 5.5-year lung cancer risk calculations aid clinicians in counselling candidates for lung cancer screening with low-dose computed tomography.

The Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study) was designed to investigate whether screening for lung cancer by low-dose multidetector computed tomography (CT) in high-risk subjects will lead to a decrease in 10-year ...

Compared with the traditional iterative reconstruction method, deep learning image reconstruction improved image quality and lung nodule detection at ultra–low-dose chest CT (0.07–0.14 mSv, similar... Background Ultra–low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction m...

Background—: Coronary calcium score (CS), traditionally based on electrocardiography-triggered computed tomography (CT), predicts cardiovascular risk. Currently, nontriggered thoracic CT is extensively used, such as in lung cancer screening. The purpose of the study was to determine the correlation in CS between nontriggered and electrocardiography-triggered CT, and to evaluate the prognostic performance of the CS derived from nontriggered CT. Methods and Results—: PubMed, Embase, and Web of Knowledge were searched until November 2012. Two reviewers independently screened 2120 records to identify studies reporting the CS in nontriggered CT and extracted information. Study quality was evaluated by standardized assessment tools. Cohen κ was extracted for agreement of CS categories between nontriggered and electrocardiography-triggered CT (validation). Hazard ratio (HR) was extracted for prognostic performance. Five studies about validation comprising 1316 individuals were included. Five studies about prognosis comprising 34 028 cardiac asymptomatic individuals, mainly from lung cancer screening trials, were included. All studies were of high quality. Meta-analysis could only be performed for validation studies because studies on prognostic performance were highly heterogeneous. Pooled Cohen κ for agreement between the 2 techniques was 0.89 (95% confidence interval, 0.83–0.95) for increasing CS categories. Increasing CS categories were associated with increasing risk of cardiovascular death or events. Nontriggered CT yielded false-negative CS in 8.8% of individuals and underestimated high CS in 19.1% of individuals. Conclusions—: Our analysis shows the prognostic value and potential role of nontriggered assessment of coronary calcium, but it does not suggest that electrocardiography-triggered CT should be replaced by nontriggered examinations.

In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.

A developed large language model generated radiologic impressions from findings in radiology reports that are professionally and linguistically appropriate for a full spectrum of radiology examinations.