Imaging of Brain Metastases: Diagnosis and Monitoring

Garcia, Gabriel; Bockel, S.; Majer, Michael; Smits, Marion

doi:10.1007/978-3-030-23417-1_12

Cited by 5 publications

(25 citation statements)

References 59 publications

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“…Gadolinium-based contrast agents's utility for MRI can be organized around two primary pathophysiologies. The first is a breakdown of the blood-brain barrier that often accompanies many structural lesions, and, in that case, GBCA extravasates into the parenchyma and enhances lesion detection (Garcia et al, 2020 ). The second is alterations in neuronal metabolism, typical of most functional disorders, in which case intravascular GBCA is used to quantify regional CBV, a cerebrovascular variable tightly coupled to energy metabolism (Belliveau, 1991 ; Gonz et al, 1995 ; Østergaard et al, 1998 ; Sugahara et al, 1998 ; Aronen et al, 2000 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains

Liu

Zhu

Sun

et al. 2022

Front. Aging Neurosci.

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While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.

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Section: Discussionmentioning

confidence: 99%

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains

Liu

Zhu

Sun

et al. 2022

Front. Aging Neurosci.

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“…GBCA's utility for MRI can be organized around two primary pathophysiologies. The first is a breakdown of the blood-brain barrier that often accompanies many structural lesions, and in which case GBCA extravasates into the parenchyma and enhances lesion detection 47 . The second is alterations in neuronal metabolism, typifying most functional disorders, in which case intravascular GBCA is used to quantify regional CBV, a hemodynamic variable tightly coupled to energy metabolism 4, 48-51 .…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Substitutes Gadolinium in Detecting Functional and Structural Brain Lesions with MRI

Liu

Zhu

Sikka

et al. 2020

Preprint

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While MRI contrast agents such as those based on Gadolinium are needed to enhance the detection of structural and functional brain lesions, there are rising concerns over their safety. Here, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced MRI datasets in mice and humans, could generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice. It was then transferred and adapted to human data, and we find that it can substitute Gadolinium-based contrast agents for detecting functional lesions caused by aging, Schizophrenia, or Alzheimer’s disease, and, for enhancing structural lesions caused by brain or breast tumors. Since derived from a commonly-acquired MRI, this framework has the potential for broad clinical utility and can be applied retrospectively to research scans across a host of diseases.

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“…MRI with and without administration of a gadolinium-based contrast agent (GBCA) is preferred to computed tomography (CT) because of its superior soft tissue contrast and increased sensitivity for detecting BMs. 1,31,32 In an emergency setting, CT is usually performed because of its wide availability and rapid accessibility; preferably, CT should be performed with and without an iodine contrast agent. 33 Optimizing the sensitivity of MRI is clinically very important because MRI findings greatly influence key decisions in therapy strategy and prognostication.…”

Section: Screening At Mrcc Diagnosismentioning

confidence: 99%

“…32 With MRI scanning at 3.0 Tesla, more (smaller) lesions are detected than at 1.5 Tesla. 32 The addition of a contrast agent increases sensitivity further. 32 Most BMs enhance in their entirety because of the lack of functioning blood-brain and blood-tumor barriers.…”

Section: Screening At Mrcc Diagnosismentioning

confidence: 99%

An interdisciplinary consensus on the management of brain metastases in patients with renal cell carcinoma

Hasanov

Yeboa

Tucker

et al. 2022

CA A Cancer J Clinicians

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Brain metastases are a challenging manifestation of renal cell carcinoma. We have a limited understanding of brain metastasis tumor and immune biology, drivers of resistance to systemic treatment, and their overall poor prognosis. Current data support a multimodal treatment strategy with radiation treatment and/or surgery. Nonetheless, the optimal approach for the management of brain metastases from renal cell carcinoma remains unclear. To improve patient care, the authors sought to standardize practical management strategies. They performed an unstructured literature review and elaborated on the current management strategies through an international group of experts from different disciplines assembled via the network of the International Kidney Cancer Coalition. Experts from different disciplines were administered a survey to answer questions related to current challenges and unmet patient needs. On the basis of the integrated approach of literature review and survey study results, the authors built algorithms for the management of single and multiple brain metastases in patients with renal cell carcinoma. The literature review, consensus statements, and algorithms presented in this report can serve as a framework guiding treatment decisions for patients. CA Cancer J Clin. 2022;72:454‐489.

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Imaging of Brain Metastases: Diagnosis and Monitoring

Cited by 5 publications

References 59 publications

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains

Deep Learning Substitutes Gadolinium in Detecting Functional and Structural Brain Lesions with MRI

An interdisciplinary consensus on the management of brain metastases in patients with renal cell carcinoma

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