Imaging of vascular cognitive impairment

Razek, Ahmed Abdel Khalek Abdel; Elsebaie, Nermeen

doi:10.1016/j.clinimag.2020.12.038

Cited by 16 publications

(10 citation statements)

References 80 publications

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“…DWI is beneficial by allowing evaluation of the vascular territory of the stroke lesions, predicting whether a patient with stroke will be benefited from the treatment 5 – 7 . Also, it might serve as a potential non-invasive biomarker for predicting stroke-related long-term complications 8 . The current gold standard for measuring the ischemic core on DWI is manual segmentation, which is a labor-intensive, time-consuming, and tedious task; therefore, it may be omitted in daily practice due to the need for instantaneous estimation.…”

Section: Introductionmentioning

confidence: 99%

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Aliş

Yergin

Alış

et al. 2021

Sci Rep

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There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist’s performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.

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Section: Introductionmentioning

confidence: 99%

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Aliş

Yergin

Alış

et al. 2021

Sci Rep

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“…MRI also is an important tool for assessing vascular brain damage [ 129 ]. While vascular dementia usually is the consequence of ischemic infarcts or haemorrhage that affect functionally important brain areas, vascular cognitive impairment is a common condition which may also contribute to neurodegenerative processes and AD especially in old patients [ 130 , 131 ]. Lesions in white matter are detected with T2-weighted MRI with high sensitivity, and with DTI such lesions and loss of fibre integrity can be localised to specific neuronal tracts.…”

Section: Imaging Techniquesmentioning

confidence: 99%

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease

Herholz

2022

Brain Sciences

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Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.

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“…His consciousness level was E4V4M6 on the Glasgow Coma Scale. Impaired attention and visuospatial cognition, simultanagnosia, and oculomotor apraxia were observed [ 3 ]. He presented with dysarthria and mild limb weakness.…”

Section: Case Presentationmentioning

confidence: 99%

Multiple cerebral infarctions associated with lung cancer-induced hypereosinophilia: a case report

et al. 2021

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Background Hypereosinophilia (HE) is caused by various conditions, including solid and hematologic tumors. Nonetheless, there exist no reports on cerebral infarctions caused by HE associated with lung cancer metastasis to the bone marrow. Case presentation We report a case of a 67-year-old man with multiple cerebral infarctions associated with HE. His white blood cell and eosinophil counts were 38,900/μL and 13,600/μL, respectively, at 4 weeks before admission. During treatment for HE, he presented with dysarthria and walking difficulties. Magnetic resonance imaging of the brain showed multiple small infarcts in regions such as the bilateral cortex, watershed area, and cerebellum. Chest computed tomography showed small nodes in the lung and enlargement of the left hilar lymph nodes. Bronchoscopic biopsy did not reveal a tumor; however, bone marrow biopsy showed infiltration of tumor cells. We considered a diagnosis of lung cancer metastasizing to the bone marrow, which induced HE and later caused cerebral infarctions. Conclusions This case report demonstrates that metastatic cancer in the bone marrow can induce HE, which can consequently cause multiple cerebral infarctions. Clinicians should consider HE as a cause of multiple cerebral infarctions in patients with cancer.

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Imaging of vascular cognitive impairment

Cited by 16 publications

References 80 publications

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease

Multiple cerebral infarctions associated with lung cancer-induced hypereosinophilia: a case report

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