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 managementJSMO Annual MeetingMarch 2026Japanese language EHR LLM extraction of longitudinal unstructured ECOG performance statusAdamson B, Zhang Y, Chamby A, et al.Publication summaryPublicationsMachine learningArtificial intelligenceJSMO Annual MeetingMarch 2026Real-world clinical characteristics and treatment patterns in patients with colorectal cancer (CRC) in JapanBando H, Ng D, Nasu I, Tajima E, Adamson B.Publication summaryPublicationsColorectal cancer