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Principal Investigator
Zhixia Su
Yangzhou University
Position Title
Not application.
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
Mar 25, 2024
A metabolomics-based multidimensional deep learning model for predicting the risk of malignancy in pulmonary nodules
Lung cancer is the second most common cancer and the leading cause of cancer-related deaths globally. Approximately half of lung cancer patients are diagnosed at an advanced stage, missing the optimal treatment window, which contributes to a significant mortality rate. Therefore, the early detection and diagnosis of lung cancer are of great public health and clinical importance. Chest X-ray and sputum cytology have been utilized for lung cancer screening since the 1970s. However, the sensitivity of these modalities was low. Over the past decades, the US National Lung Cancer Screening Trial (NLST) and several other trials in Europe have demonstrated that low-dose computed tomography (LDCT) screening effectively reduces lung cancer mortality by enabling earlier stage diagnosis. Screening for lung cancer using LDCT has led to a significant rise in the detection of solitary pulmonary nodules (PNs) in asymptomatic individuals. However, only a small fraction of these nodules are actually malignant. Therefore, it is essential to continue developing efficient and easy-to-use techniques to distinguish between benign and malignant PNs, as this is crucial for guiding clinical decision-making.
With the rapid development of histological technologies (including genomics, transcriptomics, proteomics and metabolomics), researchers have gained a more comprehensive knowledge and understanding of early screening for lung cancer. In this study, we integrated the multi-dimensional information of metabolomics and imaging, and used machine learning algorithms to construct a prediction model of malignant risk of lung nodules, which can provide a reference for the identification of malignant lung nodules and early lung cancer.

1. To analyze the epidemiological characteristics, radiological signs and other indicators between participants with benign and malignant pulmonary nodules;
2. To develop multiple factors-based prediction models for malignant risk of pulmonary nodules by using machine learning algorithms.


1.Guangyu Lu, Yangzhou University
2.Yuhang He, Yangzhou University
3.Taining Sha, Yangzhou University
4.Xiaoping Yu, The Affiliated Hospital of Yangzhou University
5.Weijuan Gong, The Affiliated Hospital of Yangzhou University