AI Medical Compendium Topic:
Brain Neoplasms

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Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment.

AJR. American journal of roentgenology
OBJECTIVE: Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector.

Journal of medical systems
Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma's can occur in many parts of the brain and...

A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.

BMC veterinary research
BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, an...

State of the Art: Machine Learning Applications in Glioma Imaging.

AJR. American journal of roentgenology
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MR...

Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks.

IEEE journal of biomedical and health informatics
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, ...

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.

Surgical oncology
Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor overall survival (OS) of patients. OS prediction of GBM patients provides useful information for surgical and treatment planning. Radiomics research attempts at predicting ...

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

Breast cancer research and treatment
PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.

MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT f...

Building medical image classifiers with very limited data using segmentation networks.

Medical image analysis
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem,...