Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Automated summary

High-dose-rate brachytherapy (HDR-BT) is a promising alternative treatment for patients with intermediate risk prostate cancer. However, there is currently no bio-marker based method to monitor treatment response in HDR-BT, and the biochemical changes that occur in hypo-fractionated HDR-BT are not well understood. This pilot study aims to assess the effectiveness of Raman spectroscopy combined with principal component analysis and random-forest classification in identifying radiation response profiles after a single dose of 13.5 Gy.