Researchers trained and validated a deep learning model that can detect subtle changes across post-treatment brain scans and forecast glioma recurrence with up to 89 percent accuracy. Artificial ...

A new liquid biopsy approach developed by Johns Hopkins Kimmel Cancer Center investigators could revolutionize brain cancer detection by identifying circulating DNA fragments from tumors and immune ...

In a recent study published in Fundamental Research, researchers propose a novel interpretable neural network model, MULGONET, based on multi-omics information analysis by deep learning to predict ...

A novel tool for rapidly identifying the genetic "fingerprints" of cancer cells may enable future surgeons to more accurately remove brain tumors while a patient is in the operating room, new research ...

One in every three people is expected to have cancer in their lifetime, making it a major health concern for mankind. A crucial indicator of the outcome of cancer is its tumor microsatellite ...

The Brain Tumor Foundation will offer free MRI screenings for early detection of brain tumors at the Sid Jacobson JCC from Sunday, June 15, through Friday, June 20. The screenings are part of the ...

New AI model demonstrates high accuracy for predicting immune checkpoint inhibitor (ICI) responsiveness by integrating tumor MSI status with stroma-to-tumor ratio Cancer remains one of the most ...