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Related Experiment Video

Updated: Jul 17, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

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

Published on: June 10, 2025

High-sensitivity Raman spectroscopy for prostate cancer detection and tissue extraction guidance.

Max J Dooley1,2, Hanlin Li1,2, Irene Low3

  • 1Department of Physics, University of Auckland, 38 Princes Street, Auckland 1010, New Zealand.

Biomedical Optics Express
|July 16, 2026
PubMed
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New classification models for prostate cancer diagnosis can reduce biopsy samples by 47% while maintaining high sensitivity and negative predictive value (NPV). This technology supports intraoperative decisions and improves diagnostic accuracy.

Area of Science:

  • Oncology
  • Medical Diagnostics
  • Spectroscopy

Background:

  • Prostate cancer diagnosis relies on fresh prostate biopsies.
  • Current methods face challenges in balancing biopsy efficiency with diagnostic accuracy.
  • Underdetection of significant disease remains a concern with existing approaches.

Purpose of the Study:

  • To develop and validate classification models for diagnosing and grading prostate cancer from fresh biopsies.
  • To compare standard classification models with application-specific models optimized for sensitivity and negative predictive value (NPV).
  • To introduce an algorithm for reducing biopsy samples while maintaining high diagnostic performance for intraoperative decision support.

Main Methods:

  • Training and validation of classification models using fresh prostate biopsy data.

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Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
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Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

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Last Updated: Jul 17, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

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

Published on: June 10, 2025

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
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Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

  • Comparison of standard models (optimized for sensitivity and specificity) with application-specific models (optimized for sensitivity and NPV).
  • Development of a 5-layer algorithm combining 5 application-specific models for enhanced performance.
  • Independent validation using two large patient cohorts.

    • Main Results:

    • Standard models achieved 80% sensitivity and 81% specificity.
    • Application-specific models were calibrated to 90% sensitivity and 95% NPV.
    • The 5-layer algorithm reduced biopsy samples by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity.
    • Models were independently validated on large patient cohorts.

    Conclusions:

    • Raman spectroscopy-based models offer a viable tool for real-time tissue analysis in diagnostic and intraoperative settings.
    • The developed algorithm can significantly reduce the number of prostate biopsy cores needed without compromising diagnostic sensitivity.
    • This technology serves as a decision-support tool, aiding pathologists and urologists in making more precise, evidence-based clinical decisions for prostate cancer management.