A research team from Memorial Sloan Kettering Cancer Center (MSK) is demonstrating that cancer outcome predictions can be improved by breaking down hospitals' traditional data silos and analyzing the information—including physicians' clinical notes—with the help of artificial intelligence (AI). A new study describes a real-time, automated approach developed at MSK that brings together doctors' free-text notes, clinical treatment and outcomes data, patient demographic data, and tumor genomic data from the MSK-IMPACT platform to identify biomarkers that can predict outcomes and likely responses to therapy. Dubbed MSK-CHORD (for Clinicogenomic Harmonized Oncologic Real-World Dataset), the effort is the largest of its kind, combing data from nearly 25,000 patients with non-small cell lung, breast, colorectal, prostate, and pancreatic cancers.
The study was led by co-first authors Justin Jee, MD, Ph.D., Christopher Fong, Ph.
D., Karl Pichotta, Ph.D.
, Thinh Ngoc Tran, Ph.D., and Anisha Luthra, and overseen by senior author Nikolaus Schultz, Ph.
D., Director of MSK's Cancer Data Science Initiative. It is published in the journal Nature .
The team found that cancer outcome predictions based on MSK-CHORD data outperformed those based on genomic data or cancer stage alone. By analyzing more than 700,000 radiology reports, MSK-CHORD was able to uncover predictors of metastasis to specific organ sites. Additionally, MSK-CHORD's size and rich annotations led the team to identify mutatio.
