Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis.

ABSTRACT: The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities

SUBMITTER: Boubnovski Martell M 

PROVIDER: S-EPMC10838282 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature