AI to Predict Cancer Treatment Success 

Researchers from the National Institutes of Health (NIH) have introduced a new AI tool dubbed as Logistic Regression-Based Immunotherapy-Response Score (LORIS) to predict response of patients to immunotherapy. 

AI Refining Cancer Treatment 

This new technology uses routine clinical, including information from basic blood tests to train the machine learning model, and to forecast the effectiveness of immune checkpoint inhibitors, which are crucial for immunotherapy in cancer treatment. 

The study led by experts from the National Cancer Institute’s (NCI) Center for Cancer Research and Memorial Sloan Kettering Cancer Center, and published in Nature Cancer, is considered a significant step in personalized medicine.  

AI VS. Traditional Methods 

For now, there are two predictive biomarkers approved by the Food and Drug Agency (FDA) used to identify patients eligible for immunotherapy. Tumor mutational burden (TMB) and PD-L1. TMB measures the number of mutations in cancer cell DNA, while PD-L1 is a protein that restricts the immune response and is targeted by some inhibitors. However, this traditional method has limitations and doesn’t always provide accurate results. 

Conversely, the new AI model LORIS collects personal information from patients, including age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio, to analyze them. It also includes the TMD, which is assessed via sequencing panels.  

Further Validation Requirements 

To validate the effectiveness of LORIS, researchers used multiple datasets of 2,881 patients that were treated with immune checkpoint inhibitors.  

Results showed that the AI model LORIS accurately foresaw the patients’ likelihood of benefiting from the treatment and estimated their overall survival and disease-free period. It has also the potential to recognize patients with low tumor mutational burden who could still benefit from immunotherapy. 

Researchers of the study acknowledge that, despite being promising in the medical field, this technology certainly has implications and requires more validation with larger studies. 

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