Raman spectroscopy with machine-learning classification for the prediction of stereotactic radiotherapy induced treatment toxicity in high-risk localised prostate cancer

Radiotherapy can lead to late-onset toxicity, to varying extents between individuals due to differences in radiosensitivity. Predicting which patients are most at risk is key to augmenting the therapeutic window. However, the underlying biological mechanisms remain poorly understood, and current experimental methods often lack clinical applicability. This study employs Raman spectroscopy to analyse biochemical profiles in peripheral lymphocytes and plasma, aiming to monitor radiotherapeutic response and predict intrinsic radiosensitivity in high-risk localised prostate cancer patients treated with stereotactic radiotherapy. Partial-least squares discriminant analysis classification of Raman spectra at baseline (n = 20) from post-hormone therapy (n = 19), mid-treatment (pre-4^th fraction; n = 21) and 3-months after treatment (n = 18) returned mean area under the curve values ranging from 0.88 to 0.93. Ensemble classifiers applied to imbalanced late toxicity datasets (grade 0–1, n = 16; grade 2+, n = 4) yielded mean F1 scores of 0.74 (random forest, lymphocytes) and 0.69 (AdaBoost, plasma); metrics based on best performing model for minority-class. Classical least squares lymphocyte and plasma toxicity models identified major concentration differences in amino acids, proteins, lipids, DNA and related biomolecules (p ' 0.05). These findings demonstrate the potential of Raman spectroscopy as a minimally invasive, objective tool for classifying blood-based biochemical profiles across radiotherapy treatment time points and distinguishing patients with late grade 0–1 and grade 2+ toxicity.