Predictive Models for Early Diagnosis of Prostate Cancer

EasyChair Preprint 13453

Abstract

Prostate cancer is a leading cause of cancer-related death among men, and early diagnosis is crucial for improving patient outcomes. However, current diagnostic methods, such as prostate-specific antigen (PSA) testing and digital rectal examination, have limitations in accurately detecting prostate cancer in its early stages. In recent years, the development of predictive models has emerged as a promising approach to enhance early diagnosis of prostate cancer.

This review article provides an overview of the various predictive modeling approaches that have been explored for the early diagnosis of prostate cancer. The key components discussed include the use of demographic and clinical factors, such as age, family history, and PSA levels, as well as the incorporation of genomic and molecular biomarkers, including gene expression signatures, epigenetic markers, and circulating tumor cells. The review also examines the implementation of multivariate predictive models, such as logistic regression, decision trees, random forests, and neural networks, and the strategies employed for model development, validation, and clinical implementation.

The potential benefits of these predictive models include improving early detection rates, reducing unnecessary biopsies, and personalizing screening and management strategies for prostate cancer. However, the review also highlights the challenges and barriers to the widespread adoption of these models, such as data availability, model interpretability, and long-term clinical outcomes.

Keyphrases: data integration, early diagnosis, machine learning, predictive modeling, prostate cancer

@booklet{EasyChair:13453,
  author    = {Godwin Olaoye},
  title     = {Predictive Models for Early Diagnosis of Prostate Cancer},
  howpublished = {EasyChair Preprint 13453},
  year      = {EasyChair, 2024}}