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Spinal Cord Neoplasms

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Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

NeuroImage. Clinical
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantificat...

Distinguishing Intramedullary Spinal Cord Neoplasms from Non-Neoplastic Conditions by Analyzing the Classic Signs on MRI in the Era of AI.

Current medical imaging
Intramedullary lesions can be challenging to diagnose, given the wide range of possible pathologies. Each lesion has unique clinical and imaging features, which are best evaluated using magnetic resonance imaging. Radiological imaging is unique with ...

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M ...

Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas.

Neuro-oncology
BACKGROUND: Prognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a fully automated deep learning (DL) pipeline for stratified overall survival (OS) prediction based on pre...

Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Brain pathology (Zurich, Switzerland)
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the exist...

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic m...

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.

Journal of X-ray science and technology
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...