Prostate Cancer Evaluation Framework: A Multivariate Approach with PLS-SEM Integration

Abstract

This research investigates prostate cancer dataset from the National Cancer Institute, US. Utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the study comprehensively explores the relationships between key diagnostic factors—prostate- specific antigen (PSA), digital rectal examination (DRE), and the size of the prostate gland. The research validates the theoretical model, emphasizing its reliability through robust statistical analyses, high inter- nal consistency, convergent validity, and substantial R-squared values. Significantly, the findings highlight the  critical impact  of  PSA,  DRE, and gland size on prostate  cancer diagnosis, emphasizing the  need for a holistic approach in clinical settings. The study not only contributes to a deeper understanding of prostate cancer but also identifies avenues for future research, suggesting the exploration of mediating variables, particularly drug dosage. Additionally, the research proposes a ground- breaking direction by advocating for a model based on personalized treatment and diagnosis, incorporating individual patient characteris- tics, genetic factors, and lifestyle considerations. This forward-looking

approach holds promise for revolutionizing prostate cancer manage- ment, improving diagnostic precision, and enabling targeted interven- tions, aligning with the evolving landscape of personalized medicine.