Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics.

Objective: To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa).

Patients And Methods: Between 20 and 50 Raman spectra were acquired from 32 fresh and non-processed post-prostatectomy specimens using a macroscopic handheld RS probe. Each measured area was characterized and categorized according to histopathological criteria: tissue type (extraprostatic or prostatic); tissue malignancy (benign or malignant); cancer grade (Grade Groups [GGs] 1-5); and tissue glandular level. The data were analysed using machine-learning classification with neural network.

Results: The RS technique was able to distinguish prostate from extraprostatic tissue with a sensitivity of 82% and a specificity of 83% and benign from malignant tissue with a sensitivity of 87% and a specificity of 86%. In an exploratory fashion, RS differentiated benign from GG1 in 726/801 spectra (91%; sensitivity 80%, specificity 91%), from GG2 in 588/805 spectra (73%; sensitivity 76%, specificity 73%), from GG3 in 670/797 spectra (84%; sensitivity 86%, specificity 84%), from GG4 in 711/802 spectra (88%; sensitivity 77%, specificity 89%) and from GG5 in 729/818 spectra (89%; sensitivity 90%, specificity 89%).

Conclusion: Current diagnostic approaches of PCa using needle biopsies have suboptimal cancer detection rates and a significant risk of infection. Standard non-targeted random sampling results in false-negative biopsies in 15-30% of patients, which affects clinical management. RS, a non-destructive tissue interrogation technique providing vibrational molecular information, resolved the highly complex architecture of the prostate and detect cancer with high accuracy using a fibre optic probe to interrogate radical prostatectomy (RP) specimens from 32 patients (947 spectra). This proof-of-principle paves the way for the development of in vivo tumour targeting spectroscopy tools for informed biopsy collection to address the clinical need for accurate PCa diagnosis and possibly to improve surgical resection during RP as a complement to histopathological analysis.