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

Authors

Max J Dooley, Hanlin Li, Irene Low, Mary L Christie, Morgan R Pokorny, Ariane Araquel-Lacamiento, Alex Porter, Kamran Zargar-Shoshtari, Claude Aguergaray

Published in

Biomedical optics express. Volume 17. Issue 7. Pages 3763-3775. Jul 01, 2026. Epub Jun 18, 2026.

Abstract

This study presents classification models trained to diagnose and grade prostate cancer using fresh prostate biopsies. We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. These results support the targeted use of Raman spectroscopy for real-time tissue analysis in diagnostic and intraoperative settings. The technology's clinical value as a decision-support tool aligns with the shared goal of pathologists and urologists to reduce the number of prostate biopsy cores while maintaining high sensitivity for clinically significant cancer. Prior studies have improved biopsy efficiency, but their performance has been variable, and concerns remain regarding underdetection of significant disease, revealing the need for approaches that improve biopsy efficiency without increasing diagnostic risk. The technology described here provides a realistic solution for targeted biopsy guidance to support more precise and evidence-based clinical decisions.

PMID:
42460332
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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