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Objectives: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing., Materials and Methods: In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC)., Results: Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size., Conclusion: The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules., Clinical Relevance Statement: Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality., Key Points: Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality., (© 2024. The Author(s).)

Objective: To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures., Methods: In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation., Results: Diagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI 1.5 to 4.0 %, p = 0.045) and trust in the algorithm was generated primarily by the certainty of the prediction (100% of participants). Different human-AI interactions were observed ranging from nearly no interaction to humanization of the algorithm. High scores in neuroticism were correlated with higher persuasibility (Pearson's r = 0.98, p = 0.02), while higher consciousness and change of accuracy showed an inverse correlation (Pearson's r = -0.96, p = 0.04)., Conclusion: Trust in the algorithm's performance was mostly dependent on the certainty of the predictions in combination with a plausible heatmap. Human-AI interaction varied widely and was influenced by personality traits., Key Points: • AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions. • Trust in the algorithm's performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap. • Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm's prediction than others., (© 2022. The Author(s).)

Objective: Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer., Methods: A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed., Results: In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters., Conclusion: Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer., Key Points: • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters., (© 2021. The Author(s).)

Purpose: The aim of this study was to evaluate the advantages of dual-layer spectral CT (DLSCT) in detection and staging of head and neck cancer (HNC) as well as the imaging of tumour margins and infiltration depth compared to conventional contrast enhanced CT (CECT)., Materials and Methods: Thirty-nine patients with a proven diagnosis of HNC were examined with a DLSCT scanner and retrospectively analysed. An age-matched healthy control group of the same size was used. Images were acquired in the venous phase. Virtual monoenergetic 40keV-equivalent (MonoE40) images were compared to CECT-images. Diagnostic confidence for tumour identification and margin detection was rated independently by four experienced observers. The steepness of the Hounsfield unit (HU)-increase at the tumour margin was analysed. External carotid artery branch image reconstructions were performed and their contrast compared to conventional arterial phase imaging. Means were compared using a Student's t-test. ANOVA was used for multiple comparisons., Results: MonoE40 images were superior to CECT-images in tumour detection and margin delineation. MonoE40 showed significantly higher attenuation differences between tumour and healthy tissue compared to CECT-images (p < 0.001). The HU-increase at the boundary of the tumour was significantly steeper in MonoE40 images compared to CECT-images (p < 0.001). Iodine uptake in the tumour was significantly higher compared to healthy tissue (p < 0.001). MonoE40 compared to conventional images allowed visualisation of external carotid artery branches from the venous phase in a higher number of cases (87% vs. 67%)., Conclusion: DLSCT enables improved detection of primary and recurrent head and neck cancer and quantification of tumour iodine uptake. Improved contrast of MonoE40 compared to conventional reconstructions enables higher diagnostic confidence concerning tumour margin detection and vessel identification., Key Points: • Sensitivity concerning tumour detection are higher using dual-layer spectral-CT than conventional CT. • Lesion to background contrast in DLSCT is significantly higher than in CECT. • DLSCT provides sufficient contrast for evaluation of external carotid artery branches.