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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Robust Logistic Regression-based Diagnosis Method of Prostate Cancer Using Optimized Feature Selection on Race

David Agustriawan¹, Vincent Kurniawan¹, Marlinda Vasty Overbeek¹

¹Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang, Indonesia.

Cancer Diagnosis & Prognosis

|November 3, 2025

Summary

This summary is machine-generated.

This study developed a race-aware prostate cancer (PCa) detection model using a 9-gene signature, achieving high accuracy in both White and Black populations. The race-specific approach enhances diagnostic fairness and accuracy for prostate cancer.

Keywords:

Prostate cancer bioinformatics feature selection gene expression logistic regression machine learning

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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

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Published on: November 2, 2013

Area of Science:

Genomics
Computational Biology
Oncology

Background:

Prostate cancer (PCa) incidence disproportionately affects Black men.
Current diagnostic methods like prostate-specific antigen (PSA) testing lack specificity.
Machine learning models often overlook racial variations in gene expression, impacting diagnostic fairness.

Purpose of the Study:

To develop a race-aware PCa detection framework.
To improve diagnostic accuracy and fairness using optimized feature selection.
To identify race-specific biomarkers for enhanced PCa diagnosis.

Main Methods:

Analysis of RNAseq-Count-STAR and clinical data from TCGA (554 patients).
Feature selection pipeline integrating Differential Gene Expression, ROC analysis, and Gene-Set Enrichment Analysis.
Development of a 9-gene logistic regression model, trained on White data and validated on Black data.

Main Results:

The 9-gene model achieved 95% accuracy in White and 96.8% accuracy in Black populations.
Fairness analysis showed minimal disparity between racial groups (4% difference in demographic parity, p=0.518).
Biologically informed feature selection enhanced model accuracy and interpretability.

Conclusions:

A race-specific PCa detection framework utilizing targeted biomarkers improves diagnostic accuracy.
The framework addresses misclassification risks inherent in race-agnostic models.
Emphasizes the importance of race-aware gene expression in machine learning diagnostics for precision medicine in PCa care.