Document Type : Original Research Paper

Authors

• A. Mohamadi
• M. Habibi
• F. Parandin

Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

Abstract

Background and Objectives: Metastatic castration-sensitive prostate cancer (mCSPC) represents a critical juncture in the management of prostate cancer, where the accurate prediction of the onset of castration resistance is paramount for guiding treatment decisions.
Methods: In this study, we underscore the power and efficiency of auto-ML models, specifically the Random Forest Classifier, for their low-code, user-friendly nature, making them a practical choice for complex tasks, to develop a predictive model for the occurrence of castration resistance events (CRE (. Utilizing a comprehensive dataset from MSK (Clin Cancer Res 2020), comprising clinical, genetic, and molecular features, we conducted a comprehensive analysis to discern patterns and correlations indicative of castration resistance. A random forest classifier was employed to harness the dataset's intrinsic interactions and construct a robust predictive model.
Results: We used over 18 algorithms to find the best model, and our results showed a significant achievement, with the developed model demonstrating an impressive accuracy of 75% in predicting castration resistance events. Furthermore, the analysis highlights the importance of specific features such as 'Fraction Genome Altered ‘and the role of prostate specific antigen (PSA) in castration resistance prediction.
Conclusion: Corroborating these findings, recent studies emphasize the correlation between high 'Fraction Genome Altered' and resistance and the predictive power of elevated PSA levels in castration resistance. This highlights the power of machine learning in improving outcome predictions vital for prostate cancer treatment. This study deepens our insights into metastatic castration-sensitive prostate cancer and provides a practical tool for clinicians to shape treatment strategies and potentially enhance patient results.

Keywords

• Metastatic Castration-sensitive Prostate Cancer
• Castration Resistance
• Random Forest Classifier
• Personalized Medicine
• Machine Learning

Main Subjects

• Machine Learning

Open Access

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Publisher

Shahid Rajaee Teacher Training University