Combining machine learning technology with neuroimaging data, clinicians will be better able to fully analyze a patient's glioblastoma brain tumor and predict cancer progression.
Eliminating Collagen 1 production in tumor cells allowed animal models of brain tumors to live longer.
Infiltrating gliomas are shaped by their genetic evolution and microenvironment, researchers report. The findings may help in the development of therapies to treat glioma brain tumors.
Study found no increased risk of developing brain tumors regardless of whether a person was a frequent cell phone user or if they had never used a cell phone before.
Asthma causes T cells to induce lung inflammation but prevents the growth of brain tumors. Reprogramming T cells in patients with brain cancer to act like T cells in those with asthma may help to curb the growth of tumors.
Researchers have successfully replicated an entire, viable glioblastoma brain tumor via 3D bioprinting. The bioprinted tumor includes a complex system of blood vessel-like tubes through which blood cells and drug molecules can flow, simulating a real tumor.
Glioblastoma can mimic the normal repair of white matter in the brain, causing the tumor to become less malignant. Additionally, a drug commonly prescribed for asthma can help suppress glioblastoma growth in mouse models.
Researchers have identified specific characteristics of vestibular schwannomas in children. While children have similar symptoms of the brain tumors as experienced by adults, the tumor size was typically larger in pediatric patients at the time of diagnosis.
Glioma brain tumors alter the function of astrocytes, possibly contributing to seizures many brain cancer patients experience. Astrocytes encasing gliomas exhibit different molecular signatures based on their proximity to the cancer cells. Those directly touching the cancer cells become elongated and swollen, mimicking the astrocyte's response to other epilepsy-related brain injuries.
A new machine learning algorithm can predict which tumors were lower-grade gliomas or glioblastoma brain cancer with a high degree of accuracy.
A new convolutional neural network that utilizes MRI brain scans can forecast genetic mutations in glioma brain tumors.
A new artificial intelligence convolutional neural network is 94.6% accurate at diagnosing real-time intraoperative brain tumors.
