Comparative Analysis of Predictive Models for Individual Cancer Risk: Approaches and Applications

This article provides a comprehensive analysis of predictive models for individual cancer risk, examining their development, application, and evaluation. The study covers various cancer types, highlighting the diversity and sophistication of models over time. Utilizing data from PubMed, Web of Science, and Scopus, the research includes models developed for 22 cancer types, with significant emphasis on breast and colorectal cancers due to their prevalence and early detection benefits. The analysis reveals an uneven distribution of models, often concentrated in the United States and the United Kingdom, with a notable gap in models for rarer cancers. Key methodologies such as logistic regression and Cox proportional hazards models dominate the field, reflecting a preference for established statistical techniques. The study underscores the importance of incorporating multiple risk factors, including genetic, environmental, lifestyle, and clinical data, to enhance predictive accuracy. Despite advancements, the article identifies a critical need for external validation and standardization in reporting practices to improve model reliability and generalizability. The findings emphasize the potential of these models in personalized cancer prevention and early detection, while also calling for continued research and methodological harmonization to address existing gaps and challenges.