English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The rich chemical information from tissue metabolomics provides a powerfulmeans to elaborate tissue physiology or tumor characteristics at cellular andtumor microenvironment levels. However, the process of obtaining suchinformation requires invasive biopsies, is costly, and can delay clinicalpatient management. Conversely, computed tomography (CT) is a clinical standardof care but does not intuitively harbor histological or prognostic information.Furthermore, the ability to embed metabolome information into CT tosubsequently use the learned representation for classification or prognosis hasyet to be described. This study develops a deep learning-based framework --tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images andtumor/normal tissue metabolite intensities to generate ten image embeddings toinfer metabolite-derived representation from CT alone. In clinical NSCLCsettings, we ascertain whether TMR-CT achieves state-of-the-art results insolving histology classification/prognosis tasks in an unseen international CTdataset of 742 patients. TMR-CT non-invasively determines histological classes- adenocarcinoma/ squamous cell carcinoma with an F1-score=0.78 and furtherasserts patients' prognosis with a c-index=0.72, surpassing the performance ofradiomics models and clinical features. Additionally, our work shows thepotential to generate informative biology-inspired CT-led features to exploreconnections between hard-to-obtain tissue metabolic profiles and routinelesion-derived image data.

Deep Representation Learning of Tissue Metabolome and Computed  Tomography Images Annotates Non-invasive Classification and Prognosis  Prediction of NSCLC