Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: A pilot study
| dc.contributor.author | Milligan, Kirsty | |
| dc.contributor.author | Deng, Xinchen | |
| dc.contributor.author | Ali-Adeeb, Ramie | |
| dc.contributor.author | Shreeves, Phillip | |
| dc.contributor.author | Punch, Samantha | |
| dc.contributor.author | Costie, Nathalie | |
| dc.contributor.author | Crook, Juanita M. | |
| dc.contributor.author | Brolo, Alexandre G. | |
| dc.contributor.author | Lum, Julian J. | |
| dc.contributor.author | Andrews, Jeffrey L. | |
| dc.contributor.author | Jirasek, Andrew | |
| dc.date.accessioned | 2023-01-13T18:39:26Z | |
| dc.date.available | 2023-01-13T18:39:26Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022 | |
| dc.description.abstract | This work combines Raman spectroscopy (RS) with supervised learning methods—group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)—to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D’Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | This work was supported by grant funding from the Natural Sciences and Engineering Research Council of Canada Discovery Grants RGPIN-2020-07232 (A.J., K.M., X.D., R.A.) and RGPIN-2020-04646 (J.L.A., P.S.), and the Canadian Institutes of Health Research (PJT 162279, J.J.L.). | en_US |
| dc.identifier.citation | Milligan, K., Deng, X., Ali-Adeeb, R., Shreeves, P., Punch, S., Costie, N., . . . Jirasek, A. (2022). “Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semisupervised learning: A pilot study.” Scientific Reports, 12(15104). https://doi.org/10.1038/s41598-022-19446-4 | en_US |
| dc.identifier.uri | https://doi.org/10.1038/s41598-022-19446-4 | |
| dc.identifier.uri | http://hdl.handle.net/1828/14657 | |
| dc.language.iso | en | en_US |
| dc.publisher | Scientific Reports | en_US |
| dc.subject | Bioanalytical chemistry | en_US |
| dc.subject | Biological physics | en_US |
| dc.subject | Cancer metabolism | en_US |
| dc.subject | Cheminformatics | en_US |
| dc.subject | Medical and clinical diagnostics | en_US |
| dc.subject | Metabolomics | en_US |
| dc.subject | Tumour biomarkers | en_US |
| dc.subject | Tumour virus infections | en_US |
| dc.subject | Urological cancer | en_US |
| dc.title | Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: A pilot study | en_US |
| dc.type | Article | en_US |