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

Prostate cancer remains one of the most prevalent malignancies affecting men worldwide, making early detection and advancements in precision medicine crucial for effective intervention and treatment. A standardized protocol is presented for utilizing stimulated Raman histology (SRH) with integrated artificial intelligence (AI) in prostate cancer detection, offering significant advancements over conventional histopathological methods. SRH provides these advancements by enhancing efficiency through near-real-time, label-free imaging of fresh, unstained tissues, thereby eliminating the delays associated with traditional biopsy analysis. By using stimulated Raman scattering (SRS) microscopy to detect the specific vibrational frequencies of CH2 bonds associated with lipids and CH3 bonds linked to proteins and DNA, cancerous and benign tissues in prostate biopsies can be differentiated. The AI model further enhances diagnostic precision, achieving 98.6% accuracy in identifying prostate cancer. The protocol outlines essential steps for sample preparation, imaging, and data analysis, facilitating improved biobanking processes and enabling downstream applications, such as transcriptomics and xenograft studies. This approach accelerates the diagnostic workflow and shows promise for intraoperative applications, potentially aiding surgeons in identifying positive margins intraoperatively. Additionally, the ability to re-scan and adjust cancer-to-tissue ratios allows for a more tailored analysis of biopsy samples, enhancing tumor detection in unprocessed tissues. Further research and validation are necessary for the widespread adoption of SRH in clinical practice.