AI may be key to streamline patient allocation to clinical trials
As a proof of concept, an AI-powered large language model matching platform shows promise in patient selection
As a proof of concept, an AI-powered large language model matching platform shows promise in patient selection
While synthetic real-world data may help overcome current challenges in clinical trials, it also introduces new complexities
The combination of lenvatinib plus everolimus improved progression-free survival in the LenCabo trial, but toxicity must be carefully assessed
In the evERA BC trial, giredestrant plus everolimus led to a prolonged progression-free survival
In early phase studies, IAG933 and VT3989 led to encouraging disease control rates with manageable toxicity
The promises of a tumour-agnostic oncology are still challenged by conventional clinical trial design and regulatory processes
Findings from latest research reflect emerging opportunities in oncology to refine treatments by challenging current paradigms of care
Several studies describe pan-cancer approaches utilising the latest technology for better patient selection and outcomes
Positive initial overall survival data were, however, attenuated and may not be clinically meaningful in a longer-term analysis
Two studies fail to meet their primary endpoints, reinforcing the need to improve the sensitivity of ctDNA testing before it enters the clinic
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