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Related Experiment Video

Updated: Sep 17, 2025

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

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

Published on: June 10, 2025

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

Lea Lough¹, Mingyu Sheng², Takeshi Namekawa²

¹Genecentrix Inc.

Journal of Visualized Experiments : Jove

|June 30, 2025

Summary

A new protocol uses stimulated Raman histology (SRH) and artificial intelligence (AI) for faster, more accurate prostate cancer detection. This label-free imaging method achieves 98.6% accuracy, improving upon traditional pathology.

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Last Updated: Sep 17, 2025

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

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

Published on: June 10, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Area of Science:

Oncology
Biotechnology
Medical Diagnostics

Background:

Prostate cancer is a leading global malignancy in men.
Early detection and precision medicine are vital for effective treatment.
Conventional histopathology has limitations in speed and sample preparation.

Purpose of the Study:

To present a standardized protocol for stimulated Raman histology (SRH) integrated with artificial intelligence (AI) for prostate cancer detection.
To demonstrate SRH's advantages over traditional histopathological methods.
To facilitate improved diagnostic workflows and downstream applications.

Main Methods:

Utilized stimulated Raman scattering (SRS) microscopy to image fresh, unstained prostate biopsy tissues.
Detected specific vibrational frequencies of lipids (CH2 bonds) and proteins/DNA (CH3 bonds) for tissue differentiation.
Developed and applied an AI model for enhanced diagnostic precision and analysis.

Main Results:

SRH provides near-real-time, label-free imaging, reducing diagnostic delays.
The integrated AI model achieved 98.6% accuracy in identifying prostate cancer.
The protocol supports improved biobanking and enables downstream applications like transcriptomics.

Conclusions:

SRH with AI offers a significant advancement in prostate cancer detection efficiency and accuracy.
The protocol accelerates the diagnostic workflow and shows potential for intraoperative margin assessment.
Further validation is required for widespread clinical adoption of SRH.