Raman spectroscopy and supervised learning as a potential tool to identify high‐dose‐rate‐brachytherapy induced biochemical profiles of prostate cancer. Issue 11 (8th August 2022)

• Record Type: Journal Article
• Authors: Milligan, Kirsty; Van Nest, Samantha J.; Deng, Xinchen; Ali‐Adeeb, Ramie; Shreeves, Phillip; Punch, Samantha; Costie, Nathalie; Pavey, Nils; Crook, Juanita M.; Berman, David M.; Brolo, Alexandre G.; Lum, Julian J.; Andrews, Jeffrey L.; Jirasek, Andrew
• Is Part Of: Journal of biophotonics. Volume 15:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-08
• Subjects: PCA -- prostate cancer -- radiation therapy -- Raman spectroscopy -- supervised learning; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202200121
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: 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. Abstract : Raman spectroscopy (RS) when combined with machine learning can be a useful tool in identifying patient response to high dose rate brachytherapy, a therapy which is poorly understood despite high levels of success in treating intermediate grade prostate cancer. Substantial consideration must be given in how to interpret RS data, including consideration of low various principal components, when applied to clinical samples.