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Glioma

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PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.

Bioinformatics (Oxford, England)
MOTIVATION: Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or c...

[A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance amide proton transfer (APT) modality data as a means to assist non-invasive diagnosis of gliomas.

Unlocking glioma genetics with deep learning.

Med (New York, N.Y.)
The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in this issue, has the potential to streamline molecular classification, intraoperative diagnosis, surgi...

Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis.

Neurosurgical focus
OBJECTIVE: Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric conne...

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

JCO clinical cancer informatics
PURPOSE: Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggr...

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Neuro-oncology
BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can...

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Briefings in bioinformatics
Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. How...

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Studies in health technology and informatics
Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molec...

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.

Neuro-oncology
BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive appr...