AI Models Predict Cancer Outcomes Using Clinical Notes and Genomic Data

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Written By Rita Wright

Scientific writer

In a groundbreaking study, researchers have developed artificial intelligence (AI) models that can accurately predict cancer outcomes, including survival rates, metastasis risk, and response to immunotherapy, by extracting valuable information from electronic health records (EHRs) and tumor-genome profiles. This novel approach, published in the prestigious journal Nature doi:10.1038/d41586-025-00335-5, represents a significant step forward in leveraging real-world data to improve cancer care and personalize treatment strategies.

AI-Powered Clinical Note Analysis

EHRs contain a wealth of unstructured data in the form of clinical notes, which often provide detailed insights into a patient’s medical history, treatment plans, and disease progression. However, manually extracting and analyzing this information is a time-consuming and resource-intensive process. The researchers developed AI models capable of accurately annotating clinical reports from more than 20,000 individuals with cancer, effectively transforming this unstructured data into structured, machine-readable formats.

By combining the AI-extracted data from clinical notes with tumor-genome profiling, the models were able to identify genomic features associated with metastasis and predict responses to immunotherapy treatments. This approach not only improves our understanding of cancer biology but also has the potential to guide personalized treatment strategies tailored to each patient’s unique genomic profile and disease characteristics.

Predicting Survival and Identifying Risk Factors

One of the most significant achievements of this study is the ability of the AI models to accurately predict overall survival rates for cancer patients. By analyzing the rich data extracted from clinical notes and genomic profiles, the models can identify patterns and risk factors associated with better or worse outcomes. This predictive capability could revolutionize cancer care by enabling physicians to make more informed treatment decisions and providing patients with more accurate prognoses.

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Furthermore, the AI models were able to identify specific genomic features that are associated with an increased risk of metastasis, the spread of cancer to other parts of the body. Early detection of these high-risk features could prompt more aggressive treatment approaches or closer monitoring, potentially improving patient outcomes and reducing the burden of metastatic disease.

Enhancing Immunotherapy Response Prediction

Immunotherapy has emerged as a promising treatment option for various types of cancer, harnessing the body’s immune system to fight cancer cells. However, not all patients respond equally well to these therapies, and predicting response remains a significant challenge. The AI models developed in this study have shown remarkable accuracy in predicting patient responses to immunotherapy treatments based on the combination of clinical note data and genomic profiling.

By identifying patients who are more likely to benefit from immunotherapy, this approach could help optimize treatment selection, reduce unnecessary side effects, and improve overall outcomes. Additionally, understanding the genomic features associated with immunotherapy response could pave the way for the development of new targeted therapies or combination strategies to enhance the effectiveness of existing treatments.

The findings of this study highlight the immense potential of AI and data-driven approaches in cancer research and clinical practice. By leveraging the vast amounts of real-world data available in EHRs and genomic databases, researchers can uncover valuable insights and develop powerful predictive models that could transform the way we diagnose, treat, and manage cancer.

Source: Nature, Published online: 05 February 2025; doi:10.1038/d41586-025-00335-5