Prevalence and prognostic effect of high tumor mutation burden (TMB-H) across multiple less common solid cancers using a real-world dataset Published September 2019 Citation Backenroth D, Shao C, Li G, Huang L,Pruitt SK, Castellanos EH, Frampton GM, Carson KR, Snow T, Singal G, Fabrizio D, Alexander BM, Jin FJ, Zhou W. . ESMO Annual Congress. . https://www.sciencedirect.com/science/article/pii/S0923753419600062 Authors:Backenroth D, Shao C, Li G, Huang L,Pruitt SK, Castellanos EH, Frampton GM, Carson KR, Snow T, Singal G, Fabrizio D, Alexander BM, Jin FJ, Zhou W Sources:ESMO Annual Congress Share Posted inPublicationsDrug discoveryTumor agnostic More publicationsESMO AI & Digital OncologyNovember 2025A pan-tumor and pan-country approach to LLM-based extraction of systemic therapies from the electronic health recordViani N, Groizard L, Harrison K, et al. Publication summaryPublicationsMachine learningTumor agnosticData managementESMO AI & Digital OncologyNovember 2025Survival prediction in advanced NSCLC (aNSCLC) amid evolving standards of care (SOC): Digital twin modeling incorporating LLM-extracted clinical contextEstevez M, Griffith S, Williams T, et al.Publication summaryPublicationsMachine learningESMO AI & Digital OncologyNovember 2025Structuring GDPR-compliant private networks to enable LLM-Extracted oncology data on pseudonymized patient EHR data in EuropeEllsworth L, Groizard L, Stefan F, et al.Publication summaryPublicationsData scienceMachine learning