Mathematical and Computational Models in Prostate Cancer: From Early Detection to Precision Therapy

Abstract

This study critically analyses the biases impacting scientific credibility and equality while looking into the main data sources used in prostate can- cer research. It examines important repositories and describes their func- tion in epidemiological and clinical analysis, including The Cancer Genome Atlas (TCGA), Surveillance, Epidemiology, and End Results (SEER), and GLOBOCAN. In particular, the under-representation of minorities and low- and middle-income groups, as well as structural biases resulting from elec- tronic health records and diagnostic access, are highlighted in the paper as limits in data completeness, representativeness, and annotation.These prob- lems limit the generalisability of sophisticated analytical techniques like ma- chine learning and spread algorithmic bias, as it is discussed. In order to improve scientific rigour and equity, the study investigates solutions such as external validation, synthetic data production, and standardised protocols. Results show that in order to facilitate thorough, equitable, and globally ap- plicable prostate cancer research, inclusive data collecting and transparent annotation are essential.