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This study explores the use of a random forest machine learning model to predict chemotherapy toxicity in patients with locally advanced cervical cancer (LACC) based on genetic variations in the PI3K/AKT pathway. By analyzing 24 single nucleotide polymorphisms (SNPs) from 259 patients, the research identifies specific genetic markers associated with increased risks of neurological, gastrointestinal, and hematological toxicities during neoadjuvant chemotherapy. The findings aim to enhance clinical treatment

Author

researchdeb2025

Language

EN