How does the "king of cancer" help the early detection of pancreatic ductal adenocarcinoma?

"Pancreatic ductal adenocarcinoma (PDAC)" is one of the most lethal types of cancer, which is characterized by rapid progression, metastasis fast, difficult early diagnosis, recurrence rate, known as the "cancer of the King"

Metabolomics can collect, detect and analyze various small molecular metabolites that are highly sensitive to biological activity and pathological conditions.

The combined diagnosis of machine learning (ML) and metabolomics is currently a very attractive and promising concept, but previous work has mainly focused on model construction, rather than selecting key metabolites for disease detection

Research results (Source: Science Advances)

In most medical applications, ML methods are usually evaluated on a data set

Traditionally, data dimensionality reduction and biomarker screening of metabolomics or lipidomics are mainly based on analysis of variance (ANOVA) and least square discriminant analysis (PLS-DA)

Tests on 1033 PDAC patients at different stages found that the accuracy of this method was 86.

The ROC curve of the ML-assisted metabolic PDAC detection method in the validation study training data set & internal validation data set & external validation data set & prospective clinical cohort (Source: Science Advances)

There are 17 types of lipids in the selected characteristic metabolites, including 4 lysophosphatidylcholines (LPC), 7 phosphatidylcholines (PC), 3 sphingomyelins (SMs), and 1 lysophosphatidylcholine Ethanolamine (LPE), 1 phosphatidylethanolamine (PE) and 1 diglyceride (DG)

Ion chromatograms of selected 17 characteristic lipid metabolites (Source: Science Advances)

This work establishes a method combining metabolomics with ML and greedy algorithms, and uses ML to refine the targeted metabolomics disease detection program

"Of course, there are some limitations of this study

The features selected by this model cannot distinguish the early or late stages of PDAC, nor can it be used to predict the prognosis of PDAC patients

Reference materials:

[1]Wang G, Yao H, Gong Y, et al.