Ten years of gadolinium retention and deposition: ESMRMB-GREC looks backward and forward

van der Molen, Aart J.; Quattrocchi, Carlo C.; Mallio, Carlo A.; Dekkers, Ilona A.; ,

doi:10.1007/s00330-023-10281-3

Cited by 13 publications

(6 citation statements)

References 87 publications

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“…It should be noted that risks associated with contrast agents such as nephrogenic systemic fibrosis or allergic reactions are generally low [ 27 ]. As far as macrocyclic GBCA are concerned, there is to date no evidence for their deposition in the brain, and even for linear GBCA, cerebral GBCA deposition has not been associated with any clinical side effects so far [ 28 ]. However, we believe that techniques to reduce the dose are highly relevant in breast MRI, especially when thinking towards its broader application in a screening setting.…”

Section: Discussionmentioning

confidence: 99%

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Müller-Franzes,

Huck,

Bode

et al. 2024

Eur Radiol Exp

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Background To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images. Methods Retrospective, ethically approved study. DDPM- and GAN-reconstructed single-slice subtraction images of 50 breasts with enhancing lesions were compared to original ones at three dose levels (25%, 10%, 5%) using quantitative measures and radiologic evaluations. Two radiologists stated their preference based on the reconstruction quality and scored the lesion conspicuity as compared to the original, blinded to the model. Fifty lesion-free maximum intensity projections were evaluated for the presence of false-positives. Results were compared between models and dose levels, using generalized linear mixed models. Results At 5% dose, both radiologists preferred the GAN-generated images, whereas at 25% dose, both radiologists preferred the DDPM-generated images. Median lesion conspicuity scores did not differ between GAN and DDPM at 25% dose (5 versus 5, p = 1.000) and 10% dose (4 versus 4, p = 1.000). At 5% dose, both readers assigned higher conspicuity to the GAN than to the DDPM (3 versus 2, p = 0.007). In the lesion-free examinations, DDPM and GAN showed no differences in the false-positive rate at 5% (15% versus 22%), 10% (10% versus 6%), and 25% (6% versus 4%) (p = 1.000). Conclusions Both GAN and DDPM yielded promising results in low-dose image reconstruction. However, neither of them showed superior results over the other model for all dose levels and evaluation metrics. Further development is needed to counteract false-positives. Relevance statement For MRI-based breast cancer screening, reducing the contrast agent dose is desirable. Diffusion probabilistic models and generative adversarial networks were capable of retrospectively enhancing the signal of low-dose images. Hence, they may supplement imaging with reduced doses in the future. Key points • Deep learning may help recover signal in low-dose contrast-enhanced breast MRI. • Two models (DDPM and GAN) were trained at different dose levels. • Radiologists preferred DDPM at 25%, and GAN images at 5% dose. • Lesion conspicuity between DDPM and GAN was similar, except at 5% dose. • GAN and DDPM yield promising results in low-dose image reconstruction. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Müller-Franzes,

Huck,

Bode

et al. 2024

Eur Radiol Exp

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“…In addition, the use of GBCA has recently been questioned over the associated costs and logistics, risk for hypersensitivity reactions, systemic nephrogenic fibrosis, and potential for accumulation in the brain [47]. Of note, no clinical symptoms have been associated with the deposition of macrocyclic GBCA [48,49]. Nevertheless, the identification of new imaging markers that can exploit endogenous contrast instead of relying on GBCA for improved breast-cancer characterization is desirable.…”

Section: Discussionmentioning

confidence: 99%

Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study

Bartsch,

Brožová,

Ehret

et al. 2024

Cancers

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Tumor neoangiogenesis is an important hallmark of cancer progression, triggered by alternating selective pressures from the hypoxic tumor microenvironment. Non-invasive, non-contrast-enhanced multiparametric MRI combining blood-oxygen-level-dependent (BOLD) MRI, which depicts blood oxygen saturation, and intravoxel-incoherent-motion (IVIM) MRI, which captures intravascular and extravascular diffusion, can provide insights into tumor oxygenation and neovascularization simultaneously. Our objective was to identify imaging markers that can predict hypoxia-induced angiogenesis and to validate our findings using multiplexed immunohistochemical analyses. We present an in vivo study involving 36 female athymic nude mice inoculated with luminal A, Her2+, and triple-negative breast cancer cells. We used a high-field 9.4-tesla MRI system for imaging and subsequently analyzed the tumors using multiplex immunohistochemistry for CD-31, PDGFR-β, and Hif1-α. We found that the hyperoxic-BOLD-MRI-derived parameter ΔR2* discriminated luminal A from Her2+ and triple-negative breast cancers, while the IVIM-derived parameter fIVIM discriminated luminal A and Her2+ from triple-negative breast cancers. A comprehensive analysis using principal-component analysis of both multiparametric MRI- and mpIHC-derived data highlighted the differences between triple-negative and luminal A breast cancers. We conclude that multiparametric MRI combining hyperoxic BOLD MRI and IVIM MRI, without the need for contrast agents, offers promising non-invasive markers for evaluating hypoxia-induced angiogenesis.

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“…Unfortunately, Gd-based agents cause potentially lethal nephrogenic systemic fibrosis [ 10 ]. Furthermore, agents containing Fe have the capacity for heavy metal toxicity in the human body and pose a risk of allergic reactions that may result in life-threatening consequences [ 11 , 12 ]. Therefore, both options pose safety risks.…”

Section: Introductionmentioning

confidence: 99%

Manganese-derived biomaterials for tumor diagnosis and therapy

Huang,

Tang,

et al. 2024

J Nanobiotechnol

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Manganese (Mn) is widely recognized owing to its low cost, non-toxic nature, and versatile oxidation states, leading to the emergence of various Mn-based nanomaterials with applications across diverse fields, particularly in tumor diagnosis and therapy. Systematic reviews specifically addressing the tumor diagnosis and therapy aspects of Mn-derived biomaterials are lacking. This review comprehensively explores the physicochemical characteristics and synthesis methods of Mn-derived biomaterials, emphasizing their role in tumor diagnostics, including magnetic resonance imaging, photoacoustic and photothermal imaging, ultrasound imaging, multimodal imaging, and biodetection. Moreover, the advantages of Mn-based materials in tumor treatment applications are discussed, including drug delivery, tumor microenvironment regulation, synergistic photothermal, photodynamic, and chemodynamic therapies, tumor immunotherapy, and imaging-guided therapy. The review concludes by providing insights into the current landscape and future directions for Mn-driven advancements in the field, serving as a comprehensive resource for researchers and clinicians.

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Ten years of gadolinium retention and deposition: ESMRMB-GREC looks backward and forward

Cited by 13 publications

References 87 publications

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study

Manganese-derived biomaterials for tumor diagnosis and therapy

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