Background: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist’s performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening. Methods: In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis. Results: In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters. Conclusion: Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.

Objectives: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. Methods: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. Results: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001). Conclusions: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.

Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias.

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

Objectives. Diagnostic guidelines for chronic obstructive pulmonary disease (COPD) are based on spirometry and clinical criteria. However, this does not address the pathophysiological complexity of the disease sufficiently. Until now, inspiratory chest computed tomography (CT) has been considered as the preferred imaging method in these patients. We hypothesized that expiratory CT may be superior to demonstrate pathophysiological changes. The aim of this prospective study was to systematically compare lung function tests with quantified CT parameters in inspiration and expiration. Materials and Methods. Forty-six patients with diagnosed COPD underwent spirometry, body plethysmography, and dose-optimized CT in maximal inspiration and expiration. Four quantified CT parameters were acquired in inspiration, expiration, and their calculated delta values. These parameters were correlated with seven established lung function parameters. Results. For inspiratory scans, a weak-to-moderate correlation with the lung function parameters was found. These correlations significantly improved when adding the expiratory scan (p<0.05). Moreover, some parameters showed a significant correlation only in expiratory datasets. Calculated delta values showed even stronger correlation with lung function testing. Conclusions. Expiratory quantified CT and calculated delta values significantly improve the correlation with lung function parameters. Thus, an additional expiratory CT may improve image-based phenotyping of patients with COPD.

The aim of this study was to systematically compare organ-specific-radiation dose levels between a radiation dose optimized perfusion CT (dVPCT) protocol of the liver and a tri-phasic standard CT protocol of the liver using a Monte-Carlo-Simulation-based analysis platform. The complete CT data of 52 patients (41 males; mean age 65 ± 12) with suspected HCC that underwent dVPCT examinations on a 3rd generation dual-source CT (Somatom Force, Siemens) with a dose optimized tube voltage of 70 kVp or 80 kVp were exported to an analysis platform (Radimetrics, Bayer). The dVPCT studies were matched with a reference group of 50 patients (35 males; mean age 65 ± 14) that underwent standard tri-phasic CT (sCT) examinations of the liver with 130 kVp using the calculated water-equivalent-diameter of the patients. The analysis platform was used for the calculation of the organ-specific effective dose (ED) as well as global radiation-dose parameters (ICRP103). The organ-specific ED of the dVPCT protocol was statistically significantly lower when compared to the sCT in 14 of 21, and noninferior in a total of 18 of 21 examined items (all p < 0.05). The EDs of the dVPCT examinations were especially in the dose sensitive organs such as the red marrow (17.3 mSv vs 24.6 mSv, p = < 0.0001) and the liver (33.3 mSv vs 46.9 mSv, p = 0.0003) lower when compared to the sCT. Our results suggest that dVPCT performed at 70 or 80 kVp compares favorably to sCT performed with 130 kVp with regard to effective organ dose levels, especially in dose sensitive organs, while providing additional functional information which is of paramount importance in patients undergoing novel targeted therapies.