Leveraging AI Predictive Models and Genomic Biomarkers in Prostate Cancer Management

Prostate cancer is one of the major challenges facing healthcare systems and requires the achievement of personalized medicine for men in order to properly address treatment response heterogeneity and biochemical recurrence rates. Drawing on the results of applying advanced machine learning models and deep neural networks such as DeepSurv, this policy brief explains the potential of radiogenomics in accurately predicting treatment outcomes. Evidence indicates that the simultaneous use of quantitative features extracted from MRI images and key genetic biomarkers, including Ki-67, PTEN, and the Decipher index, provides a valuable opportunity for non-invasive prediction of disease recurrence with high accuracy. Focusing on performance indicators, the findings of this study confirm that replacing or augmenting conventional diagnostic methods with interpretable AI tools can enable intelligent classification of patients into high-risk and low-risk groups before the initiation of radiotherapy. Implementing this approach at the level of macro health policy can lead to a reduction in unnecessary treatments, better management of side effects, and optimized financial resources. Moreover, by enabling adaptive and personalized radiotherapy, it can significantly improve survival rates and patients’ quality of life. In this regard, three policy strategies were proposed and analyzed: “defining radiogenomics in screening protocols,” “personalizing radiotherapy dose based on DeepSurv risk,” and “establishing a national prostate radiogenomics data network.” Ultimately, this document emphasizes the need to strengthen clinical decision-making and employ intelligent decision support systems to achieve treatment equity and efficient cancer management.