Milligan, KirstyDeng, XinchenAli-Adeeb, RamieShreeves, PhillipPunch, SamanthaCostie, NathalieCrook, Juanita M.Brolo, Alexandre G.Lum, Julian J.Andrews, Jeffrey L.Jirasek, Andrew2023-01-132023-01-1320222022Milligan, 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/s41598-022-19446-4https://doi.org/10.1038/s41598-022-19446-4http://hdl.handle.net/1828/14657This 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.enBioanalytical chemistryBiological physicsCancer metabolismCheminformaticsMedical and clinical diagnosticsMetabolomicsTumour biomarkersTumour virus infectionsUrological cancerArticle