A protein designed by Cedars-Sinai Cancer investigators can cross the protective blood-brain barrier safely and deliver therapy directly into cancerous tumor cells, a preclinical study shows.
Ze’ev Ronai, a cancer investigator and director of the Translational Research Institute at Cedars-Sinai, received an Outstanding Investigator Award from NCI.
Cedars-Sinai performed the first robot-assisted microsurgical head-and-neck cancer reconstructive surgery in the United States after the robot device received FDA approval for the procedure.
A study led by Cedars-Sinai investigators provides evidence that thyroid cancer continues to be overdiagnosed and that aggressive screening and treatment of thyroid cancer has not led to higher survival rates.
Over the past five years, Cedars-Sinai Cancer has built an integrated, regional system designed to provide cancer care close to where patients live and work. This model of care, directed by an academic medical center to patients at the community level, proved to be the best possible approach to supporting patients in our 11-million-person catchment area during the worst fire disaster in California history.
On Jan. 7, a bit after 6 p.m., Ravi Salgia was at his Eaton Canyon home, at the edge of Angeles National Forest.
Cedars-Sinai investigators who previously developed an imaging tool that used artificial intelligence to predict pancreatic cancer are now working to adapt that tool specifically for Black patients, who have disproportionately high rates of the disease.
The NCI Cancer Center Support Grant requires community outreach and engagement, but the design and implementation of COE programs, as well as staff training, are largely left to individual institutions.
Jupiter Bioventures, a biotechnology venture foundry that builds science-driven therapeutics companies, announced the closing of a $70 million initial financing.
A group of investigators led by Cedars-Sinai have developed and successfully tested a new artificial intelligence method to make launching cancer clinical trials easier and faster. The method uses patients’ pathology reports to automate the classification of patients by the severity of their cancers, potentially shortening the process of selecting candidates for clinical trials.
