A novel, multi-analyte test developed by researchers at the Johns Hopkins Kimmel Cancer Center, its Ludwig Center and the Johns Hopkins Department of Neurosurgery can accurately identify brain cancers ...

Over the past decade, advancements in machine learning (ML) and deep learning (DL) have revolutionized segmentation accuracy.

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 ...

Researchers propose an innovative deep learning model for accurately predicting MSI tumor and immune checkpoint inhibitor responsiveness.

MANILA, Philippines — The Philippine General Hospital (PGH) is studying the viability of using Taiwanese artificial intelligence (AI) software that can detect brain tumors in five minutes. PGH ...

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 ...

Tumor features were compared between paired primary tumors and their synchronous CRLMs using the Wilcoxon signed-rank test overall and within subgroups. Linear regression models were used to find ...