Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer

TL;DR

This study applies machine learning and deep learning techniques to RNA sequencing data to improve the accuracy of prostate cancer staging, demonstrating promising results that could enhance clinical diagnostics.

Contribution

It introduces a comprehensive AI-based framework combining feature selection, machine learning, and deep learning for more accurate prostate cancer staging using TCGA data.

Findings

Random Forest achieved an 83% F1-score.

Deep learning with data augmentation reached 71.23% accuracy.

PCA-based reduction achieved 69.86% accuracy.

Abstract

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA) testing, and biopsies underscore the critical importance of accurate staging detection in enhancing treatment outcomes and improving patient prognosis. This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The performance of the study is measured with respect to the F1-score, as well as precision and recall, all of which are calculated as weighted averages. The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm, followed by Logistic Regression at 80%, while both Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore, deep learning models with data augmentation achieved an accuracy of 71. 23%, while PCA-based dimensionality reduction reached an accuracy of 69.86%. This research highlights the potential of AI-driven approaches in clinical oncology, paving the way for more reliable diagnostic tools that can ultimately improve patient outcomes.