Differences in hypersensitivity reactions and gadolinium deposition disease/symptoms associated with gadolinium exposure to gadolinium-based contrast agents: new insights based on global databases VigiBase, FAERS, and IQVIA-MIDAS.

BACKGROUND: Hypersensitivity reactions (HSRs) can occur unexpectedly and be life-threatening when gadolinium-based contrast agents (GBCAs) are used. Gadolinium deposition disease (GDD) and symptoms associated with gadolinium exposure (SAGE) have been controversial for a long time. However, similar studies are currently incomplete or outdated. Therefore, comparing the safety of different GBCAs in terms of HSRs and GDD/SAGE using the latest post-marketing safety data should yield further insights into safely using GBCAs. METHODS: The safety differences between all GBCAs to GDD and the spectrum of GBCA-related HSRs were all compared and analyzed by using the World Health Organization database VigiBase and the FDA Adverse Event Reporting System (FAERS) database in this study. A further analysis of SAGE was also conducted using FAERS data. The lower limit of the reporting odds ratio (ROR) 95% confidence interval was used for signal detection. Moreover, the frequency of HSRs was calculated by dividing the number of reports in VigiBase by the total sales volume (measured in millions) from 2008 to 2022 in the IQVIA Multinational Integrated Data Analysis System. All adverse events were standardized using the Medical Dictionary for Drug Regulatory Activities (MedDRA) 26.0. RESULTS: This study shows that all GBCAs have the potential to induce HSRs, with nonionic linear GBCAs exhibiting a comparatively lower signal. According to standardized MedDRA query stratification analysis, gadobutrol had a greater ROR025 for angioedema. The ROR025 of gadobenate dimeglumine and gadoteridol is larger for anaphylactic/anaphylactoid shock conditions. Regarding severe cutaneous adverse reactions, only gadoversetamide and gadodiamide showed signals in FAERS and VigiBase. There were also differences in the frequency of HSRs between regions. Regarding GDD, gadoterate meglumine, and gadoteridol had a lower ROR025. An analysis of the 29 preferred terms linked to SAGE indicated that special consideration should be given to the risk of skin induration associated with gadoversetamide, gadopentetate dimeglumine, gadobenate dimeglumine, gadodiamide, and gadoteridol. Additionally, gadodiamide and gadoteridol pose a greater risk of skin tightness compared to other GBCAs. CONCLUSIONS: The risk differences among GBCAs using data from several sources were compared in this study. However, as a hypothesis-generating method, a clear causal relationship would require further research and validation.

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.

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 methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.