We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
High accuracy of ex vivo identification of prostate cancer in radical prostatectomy specimens with Raman spectroscopy.
- Authors
Trinh, Vincent Q.; Aubertin, Kelly; Vladoiu, Maria C.; Grosset, Andrée-A.; Saint-Pierre, Catherine; Saad, Fred; Jermyn, Mike; Leblond, Frédéric; Trudel, Dominique
- Abstract
Objective: Biopsies for prostate cancer (PCa) are prone to Gleason score (GS) upgrading after radical prostatectomy (RP) due to undersampling. This discrepancy can be reduced with a novel label-free and non-destructive tissue spectral analysis technique, Raman spectroscopy (RS). Our pilot study consists in identifying PCa through RS in ex vivo prostate. Method: We included patients undergoing RP for PCa. A 4mm prostate section is sampled <2 hours post-RP. Approximately 40 spectral acquisitions are performed with a multi-wavelength hand-held Raman probe that has a diameter of 500 microns. Each acquisition point is inked and the section is processed for histological analysis by H&E staining. Each point is associated to spectral acquisitions by creating a correlation area (CA) that compensated for positional variations by adding a 1000 microns buffer zone. Preliminary histological data components for each CA included benign, benign hyperplasia, cancer, cancer variant and GS. Spectral acquisitions are analyzed by Raman-specific machine learning methods in order to identify cancer. Data and results: Our pilot study included 7 patients, totaling 220 acquisitions. 74 CA were fully delimitated in a PCa nodule and 146 in benign tissue. After processing through machine learning algorithms, RS achieved a 76% sensitivity, 99% specificity and 91% accuracy for cancer detection with the leave-one-out procedure. Conclusion: Preliminary data suggests that Raman spectroscopy in ex vivo RP specimens shows high accuracy for PCa detection. Our results are being fully explored in a 50 patient study that will additionally propose novel algorithms for GS stratification and cancer density testing.
- Publication
Canadian Journal of Pathology, 2016, Vol 8, p44
- ISSN
1918-915X
- Publication type
Article