biostudies-literatureUnknown5(3)Liu RStanford UniversityRochePatients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict whichCell reports. Medicine101444https://www.ebi.ac.uk/biostudies/studies/S-EPMC10983036biostudies-literatureSystematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.PMC10983036Zou JWaliany SMcGough SHuang ZRizzo SLin YGCopping RLiu RGarmhausen MRNeal JPal NWang LfalseSystematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which2024-01-01T00:00:00Z2024 Mar2025-05-29T19:27:57.643Z2025-05-29T19:27:57.643ZS-EPMC109830363842842610.1016/j.xcrm.2024.101444