Encoding LC–MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis

Abstract

Liquid chromatography−mass spectrometry (LC−MS)based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC−MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC−MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC−MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

Review on analytical technologies and applications in metabolomics

“…In recent years, LC-MS has been widely applied in metabolomics for clinical research [95,96]. In this way, biomarkers and drugs of many diseases in animals and humans are found to interfere with diseases.…”

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

“…Metabolomics analysis can be performed using GC-MS and LC-MS, and LC-MS is commonly used for the analysis of lipidomics. The combination of metabolomics and AI has flourished in various areas of cancer, including breast cancer [165,166], head and neck cancer [167], colorectal cancer [168,169], glioma cancer [170], esophageal cancer [171,172], lung cancer [52,173], kidney cancer [174], and neuroendocrine tumors [175]. With the greatest prediction accuracy (AUC = 0.93) and a deeper understanding of disease biology, a DL technique has been shown to be beneficial for metabolomics-based breast cancer ER status categorization [176].…”