How will computational and AI tools guide future anticancer strategies?
Growing evidence shows that AI models can predict a patient's disease trajectory and signal the need for early therapeutic intervention
Study presents an AI-guided approach to target cancer antigens
The innovative approach was successfully tested on NY-ESO-1 and promises to bypass some current challenges in the identification of T cell receptors for therapeutic use
Cancer Control Principles
AI is impacting cancer care, but also the environment
Research shows that widespread use of artificial intelligence tools is associated with high demand for energy, water and minerals
Integrating multiomic spatial profiling and ctDNA for a deeper understanding of cancer complexity
Diverse approaches are investigated in oncology that also include the use of artificial intelligence
Cancer Intelligence (eHealth, Telehealth Technology, BIG Data)
Will AI reduce or increase inequities in cancer care?
While the use of artificial intelligence systems is expanding in oncology, ethical issues including biases introduced in care delivery and uneven access to technology raise concerns about whether some patients may be left behind
The updated ESMO-MCBS captures the evolving treatment landscape and patient priorities
The ESMO-Magnitude of Clinical Benefit Scale has been enhanced to address critical aspects of modern oncology trials, such as novel study designs, quality of life and long-term survival benefits
Machine learning approach identifies the enzyme MMP2 as a key target for drug repurposing in advanced cervical cancer
Additional treatment targets and potentially repurposable agents were identified based on chemoradiotherapy response, which could now be explored for further validation.
Cancer Control Principles
Microplastics and cancer: a microscopic threat, a macro challenge
Evidence is growing for a potential role of microplastics in cancer development, but more solid research is needed
AI applications in oncology go beyond clinical practice
While many models and tools are designed to support clinical decision-making, AI is already serving cancer research for background tasks, with some potential to educate patients too
Machine learning models predict adverse events and survival in patients receiving immunotherapy
New data supports the use of artificial intelligence for the administration of immune-check inhibitors in solid tumours