Development of Molecular Digital Twins Based on Ambient Ionization Mass Spectrometry Imaging for Application in Cancer Surgery DOI Creative Commons
Yanis Zirem, Léa Ledoux, Nina Ogrinc

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Summary Cancer surgery is a fundamental component of oncology treatment, its quality significantly impacts patient outcomes, influencing both relapse rates and survival. However, achieving this customization contingent upon early collection robust molecular data during surgery, providing accurate information for diagnosis, prognosis, delineating surgical margins. The introduction digital twin (DT) technology has recently opened new era precision effectiveness in cancer surgery. Expanding from successful implementations the industrial sector, DT concept evolved into highly promising breakthrough healthcare. Therefore, our study goal on creating by using high-throughput obtained through mass spectrometry imaging. We developed machine-learning-based pipeline that allow to depict infiltration cells normal tissue offer precise delineation tumor margins thanks SpiderMass. This process also enables prediction relative presence bacterial strains tumoral healthy mammary glands.

Language: Английский

Heterogeneity Assessment and Protein Pathway Prediction via Spatial Lipidomic and Proteomic Correlation: Advancing Dry Proteomics concept for Human Glioblastoma Prognosis DOI Creative Commons
Laurine Lagache, Yanis Zirem, Émilie Le Rhun

et al.

Molecular & Cellular Proteomics, Journal Year: 2024, Volume and Issue: 24(1), P. 100891 - 100891

Published: Dec. 6, 2024

Language: Английский

Citations

0
Development of Molecular Digital Twins Based on Ambient Ionization Mass Spectrometry Imaging for Application in Cancer Surgery DOI Creative Commons
Yanis Zirem, Léa Ledoux, Nina Ogrinc

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Summary Cancer surgery is a fundamental component of oncology treatment, its quality significantly impacts patient outcomes, influencing both relapse rates and survival. However, achieving this customization contingent upon early collection robust molecular data during surgery, providing accurate information for diagnosis, prognosis, delineating surgical margins. The introduction digital twin (DT) technology has recently opened new era precision effectiveness in cancer surgery. Expanding from successful implementations the industrial sector, DT concept evolved into highly promising breakthrough healthcare. Therefore, our study goal on creating by using high-throughput obtained through mass spectrometry imaging. We developed machine-learning-based pipeline that allow to depict infiltration cells normal tissue offer precise delineation tumor margins thanks SpiderMass. This process also enables prediction relative presence bacterial strains tumoral healthy mammary glands.

Language: Английский

Citations

0