AI Tool Predicts Cancer Patients’ Response to Immunotherapy

The machine-learning model, named Logistic Regression-Based Immunotherapy-Response Score (LORIS), was developed by scientists at the National Cancer Institute (Bethesda, MD, USA). It aims to assist doctors in determining the efficacy of immunotherapy drugs for a patient's cancer treatment. The AI model bases its predictions on five clinical features routinely collected from patients: age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio, an indicator of inflammation. The model also considers tumor mutational burden, evaluated through sequencing panels.

This model was built and validated using data from multiple independent datasets comprising 2,881 patients treated with immune checkpoint inhibitors across 18 types of solid tumors. The model accurately predicted both a patient’s likelihood of responding to an immune checkpoint inhibitor and their overall survival time, including the period before disease recurrence. Remarkably, the model also identified patients with low tumor mutational burden who could still benefit from immunotherapy. The findings of the study were published in Nature Cancer on June 3, 2024. The researchers emphasized the need for larger prospective studies to further validate the AI model in clinical settings and have made it publicly accessible. 

Related Links:
National Cancer Institute
LORIS