AI algorithm predicts response to checkpoint Inhibitors!

AI algorithm predicts response to checkpoint Inhibitors!
I know, I know you've heard it many times. But this one is different.

It's a collab between Dr. Eytan Ruppin of NCI and Dr. Luc Morris of MSKCC.

The dataset contained 2,880 cancer patients with 18 different types of solid tumors.

The study assessed 20+ clinical, pathologic, and genomic features.

After developing and testing different machine learning models, the team created LORIS (logistic regression-based immunotherapy-response score).

It is based on the patient’s age, cancer type, history of cancer therapy, blood albumin (a protein made by the liver), blood NLR (a measure of inflammation), and tumor mutational burden (TMB).

LORIS predicts patient outcomes following immunotherapy for both pan-cancer and individual cancer types.

LORIS successfully identifies low TMB or low PD-L1 TPS patients who can benefit from immunotherapy.

LORIS requires validation in further large datasets, but it could be a valuable tool for improving clinical decision-making practices in precision medicine to maximize patient benefit.

Why is it different? It's accessible...

The tool is available on the NCI website here
https://loris.ccr.cancer.gov/

Chang TG, Cao Y, Sfreddo HJ, Dhruba SR, Lee SH, Valero C, Yoo SK, Chowell D, Morris LGT, Ruppin E. Nat Cancer. 2024