An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis.

ABSTRACT: Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent appr

SUBMITTER: Bellantuono L 

PROVIDER: S-EPMC10547772 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature