Data Integration

"Researchers have also applied techniques from natural language processing (NLP)—a field of AI that includes tools, such as LLMs, that allow computers to understand and use human language—to extract information from EHRs and clinical notes to combine with genomic, demographic, and treatment data. Using data from nearly 25,000 cancer patients, this approach improved predictions of cancer outcomes, such as survival and risk of metastasis, compared to traditional staging or genomic data alone (1059)Jee J, et al. (2024) Nature, 636: 728..\n\nIn another study, researchers trained a DL model on data from over 15,000 patients across 38 types of solid tumors to identify indicators of how cancer will progress, and which treatment may be most effective. They uncovered more than 1,300 novel connections between diverse data types—such as body composition, lab tests, and tumor genetics—to uncover mechanisms that help determine how a patient’s disease is likely to progress (1060)Keyl J, et al. (2025) Nat Cancer, 6: 307.. These findings show the promise of AI in aiding clinicians in prioritizing and optimizing critical patient-specific information and treatment strategies.\n\nLeveraging a novel ML platform that combines drug response data with molecular features from the patient’s own cancer cells, researchers have uncovered biomarkers of chemotherapy resistance and identified potential alternative treatments for colorectal cancer patients. The platform accurately predicted clinical outcomes across four diverse cohorts and proposed alternative therapies for treatment-resistant tumors (1061)Chia S, et al. (2025) Cell Rep Med, 6: 102053.. Another AI tool integrated multi-omic data from lung cancer patients to predict personalized treatment responses, suggest new drug uses, and estimate how well treatments might work and their potential side effects (1062)Butt RN, et al. (2025) Sci Rep, 15: 15080..\n\nAI-guided platforms can also aid in determining how to adjust treatment doses over time. In a recent study, researchers developed an AI tool to guide individualized chemotherapy dosing recommendations based on a patient’s treatment response data (1063)Blasiak A, et al. (2025) NPJ Precis Oncol, 9: 49.. The platform enabled real-time dose adjustments and was adaptable to patient-specific changes such as toxicity levels and tumor response, offering a promising path toward safer, more personalized cancer chemotherapeutic regimens."