Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment. Described in a paper published in “Nature Communications,” the tool, called ...

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 ...

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 ...

Artificial intelligence (AI) is steadily reshaping the country’s healthcare landscape, offering transformative solutions across diagnostics, patient triage, and hospital operations.

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 ...

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 ...

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 ...

Find out how AI models use speech to screen for brain diseases early and efficiently. Explore new diagnostic possibilities by ...