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 managementASCO Annual MeetingMay 2026Real-world biomarker testing rates, targeted therapy use, and clinical outcomes in patients with advanced gastric or GEJ cancer in the United StatesDamato L, Liao NS, Bryan J, Hankinson EA, Castellanos E.Publication summaryPublicationsPrecision medicineGastric/Esophageal cancerASCO Annual MeetingMay 2026From plenary to practice: A large language model (LLM)-based thematic analysis of landmark clinical trial discussions and factors influencing their real-world adoptionCohen AB, Williams T, Aggarwal C, et al.Publication summaryPublicationsMachine learningArtificial intelligence