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Updated: Jun 5, 2026
08:05
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
Machine Learning-Integrated Raman Spectroscopy Identifies Race-Associated Biochemical Signatures in Prostate Cancer
Maria Iftesum1, Gyana Ranjan Sahoo2, Elnaz Sheikh1
1Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA.
Journal of Biophotonics
|June 4, 2026
Summary
Raman spectroscopy reveals distinct biochemical differences in prostate tumors between Black and White men, offering insights into prostate cancer racial disparities. This label-free technique shows promise for identifying aggressive disease markers.
Area of Science:
- Biochemistry
- Medical Spectroscopy
- Oncology
Background:
- Black men face higher prostate cancer incidence and mortality.
- Biochemical factors contributing to these disparities are not fully understood.
Purpose of the Study:
- To investigate molecular differences in prostate tissues between Black and White patients using Raman spectroscopy.
- To identify biochemical markers associated with prostate cancer racial disparities.
Main Methods:
- Utilized Raman spectroscopy on formalin-fixed, paraffin-embedded (FFPE) prostate tissues.
- Applied independent component analysis-partial least squares (ICA-PLS) for spectral correction and wavelet denoising.
- Employed Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for quantifying cellular components.
- Trained Random Forest (RF) models for cancer versus control tissue classification.
Main Results:
- MCR-ALS identified elevated protein, collagen, lipid, and nucleic acid signatures in tumors from Black patients.
- RF models achieved over 90% accuracy, 95% sensitivity, 85% specificity, and an AUC > 0.96 for tissue classification.
- Biochemical profiles correlated with clinically observed aggressive disease phenotypes.
Conclusions:
- Raman spectroscopy combined with computational analysis offers a powerful label-free method to study prostate cancer.
- This approach can probe biochemical drivers underlying racial disparities in prostate cancer.
- Findings support the potential of spectroscopic methods for diagnosing and understanding prostate cancer aggressiveness.
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