[Strategic key points and cases of study designs for prediction models in oncology]

The development of artificial intelligence technologies, the promotion of precision medicine concepts, and the widespread application of electronic health data and multi-omics data have collectively advanced the use of clinical prediction models as essential tools for supporting medical decision-making in oncology research. However, despite the rapid growth in related research, much research remains difficult to implement in clinical practice due to methodological inconsistencies and limited evidence quality. Ensuring the scientific rigor, interpretability, and clinical utility of prediction models has become a key challenge for researchers. Taking the field of oncology as an example, this paper systematically reviews the common types and whole framework of prediction model research and explores relevant methodological principles, common challenges, and pitfalls across key stages, including research topic selection, study design, and study implementation, to provide methodological guidance for oncology prediction model research.