Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.

ABSTRACT: Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tiss

SUBMITTER: Fuentes AM 

PROVIDER: S-EPMC9883395 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature