  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 to generate ten image embeddings to
infer metabolite-derived representation from CT alone. In clinical NSCLC
settings, we ascertain whether TMR-CT achieves state-of-the-art results in
solving histology classification/prognosis tasks in an unseen international CT
dataset of 742 patients. TMR-CT non-invasively determines histological classes
- adenocarcinoma/ squamous cell carcinoma with an F1-score=0.78 and further
asserts patients' prognosis with a c-index=0.72, surpassing the performance of
radiomics models and clinical features. Additionally, our work shows the
potential to generate informative biology-inspired CT-led features to explore
connections between hard-to-obtain tissue metabolic profiles and routine
lesion-derived image data.
