Simultaneous assessment of heart and lungs with gated high-pitch ultra-low dose chest CT using artificial intelligence-based calcium scoring.

Purpose: The combined testing for coronary artery and pulmonary diseases is of clinical interest as risk factors are shared. In this study, a novel ECG-gated tin-filtered ultra-low dose chest CT protocol (GCCT) for integrated heart and lung acquisition and the applicability of artificial intelligence (AI)-based coronary artery calcium scoring were assessed. Methods: In a clinical registry of 10481 patients undergoing heart and lung CT, GCCT was applied in 44 patients on a dual-source CT. Coronary calcium scans (CCS) with 120 kVp, 100 kVp, and tin-filtered 100 kVp (Sn100) of controls, matched with regard to age, sex, and body-mass index, were retrieved from the registry (ntotal=176, 66.5 (59.4-74.0) years, 52 men). Automatic tube current modulation was used in all scans. In 20 patients undergoing GCCT and Sn100 CCS, Agatston scores were measured both semi-automatically by experts and by AI, and classified into six groups (0, <10, <100, <400, <1000, ≥1000). Results: Effective dose decreased significantly from 120 kVp CCS (0.50 (0.41-0.61) mSv) to 100 kVp CCS (0.34 (0.26-0.37) mSv) to Sn100 CCS (0.14 (0.11-0.17) mSv). GCCT showed higher values (0.28 (0.21-0.32) mSv) than Sn100 CCS but lower than 120 kVp and 100 kVp CCS (all p < 0.05) despite greater scan length. Agatston scores correlated strongly between GCCT and Sn100 CCS in semi-automatic and AI-based measurements (both ρ = 0.98, p < 0.001) resulting in high agreement in Agatston score classification (κ = 0.97, 95% CI 0.92-1.00; κ = 0.89, 95% CI 0.79-0.99). Regarding chest findings, further diagnostic steps were recommended in 28 patients. Conclusions: GCCT allows for reliable coronary artery disease and lung cancer screening with ultra-low radiation exposure. GCCT-derived Agatston score shows excellent agreement with standard CCS, resulting in equivalent risk stratification.

Incidental Pulmonary Nodules - What Do We Know in 2022.

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, and early LC diagnosis can significantly improve outcomes and survival rates in affected patients. Implementation of LC screening programs using low-dose computed tomography CT in high-risk subjects aims to detect LC as early as possible, but so far, adoption of screening programs into routine clinical care has been very slow. In recent years, the use of CT has significantly increased the rate of incidentally detected pulmonary nodules. Although most of those incidental pulmonary nodules (IPNs) are benign, some of them represent early-stage LC. Given the large number of IPNs detected in the range of several millions each year, this represents an additional, maybe even larger, opportunity to drive stage shift in LC diagnosis, next to LC screening programs. Comprehensive evaluation and targeted work-up of IPNs are mandatory to identify the malignant nodules from the crowd, and several guidelines provide radiologists and physicians' guidance on IPN assessment and management. However, IPNs still seem to be inadequately processed due to various reasons including insufficient reporting in the radiological report, missing communication between stakeholders, absence of patient tracking systems, and uncertainty regarding responsibilities for the IPN management. In recent years, several approaches such as lung nodule programs, patient tracking software, artificial intelligence, and communication software were introduced into clinical practice to address those shortcomings. This review evaluates the current situation of IPN management and highlights recent developments in process improvement to achieve first steps toward stage shift in LC diagnosis.