Application of Biostatistics in Cancer Research

Dear Colleagues,

Advances in statistical methods and cancer research are intrinsically linked, driving forward innovations in both fields. In an era where experimental therapies are increasingly expensive, cutting-edge and efficient clinical trial designs are crucial. These designs can significantly reduce costs and expedite the journey of successful treatments to the market.

Emerging technologies in radiomics, genomics, proteomics, metabolomics, and spatial transcriptomics demand sophisticated statistical and bioinformatics approaches. These include graphical models, machine learning, and artificial intelligence (AI). Moreover, new statistical methods in genome-wide association studies (GWASs) are instrumental in identifying individuals at increased risk of cancer, thereby enhancing prevention strategies and improving early detection through screening.

Additionally, the application of statistical approaches to natural language processing (NLP)—a technology that translates human language into machine-readable data—is a pioneering area in cancer research. This Special Issue will showcase breakthrough statistical methods poised to make a significant impact on advancing cancer research.

We look forward to receiving your contributions.


Prof. Dr. Alan Hutson
Dr. Han Yu
Guest Editors