Debiao Li, Ph.D.

Cedars-Sinai Cancer Leading Artificial Intelligence Study to Identify Risk of Pancreatic Cancer in Vulnerable Group

Cedars-Sinai investigators who previously developed an imaging tool that used artificial intelligence (AI) to predict pancreatic cancer are now working to adapt that tool specifically for Black patients, who have disproportionately high rates of the disease.

“The incidence of pancreatic cancer among the Black population is at least 50% higher than the incidence of other racial groups. Furthermore, research has shown that Black patients have the lowest survival rate,” said Debiao Li, PhD, director of the Biomedical Imaging Research Institute and professor of Biomedical Sciences and Imaging at Cedars-Sinai, and co-principal investigator of the study. “We know that there are genetic, socioeconomic and lifestyle differences between ethnic and racial populations, and we suspect some of these differences might affect pancreatic tissue and pancreatic cancer risk… Continue reading.

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases.

“The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all,” explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. “AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases… Continue reading.