SERS-Based Serum Molecular Spectral Screening for Benign and Malignant Pulmonary Proliferative Nodules

Pulmonary nodules are often an early indicator of lung cancer. With the widespread adoption of chest CT scans in routine physical examinations, an increasing number of pulmonary nodules are being detected, including a variety of small nodules such as inflammatory lesions, benign tumors, and malignant tumors. Currently, there is no unified international consensus on the diagnostic and treatment strategies for pulmonary nodules, as outlined by various global guidelines. Developing and implementing a comprehensive lung nodule and lung cancer screening program within public health management systems remains a complex and challenging endeavor. Advancing research and proposing lung cancer screening technologies that are highly sensitive, highly specific, simple, accessible, and cost-effective is an essential and pressing priority in modern healthcare. Raman spectroscopy (RS), as a non-invasive and highly specific molecular detection technique, can be obtained at the molecular level to sensitively detect changes in biomolecules composed of proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples. The surface enhanced Raman spectroscopy (SERS) developed based on this technology is one of the feasible methods for high-sensitivity biomolecule analysis. Although SERS technology has shown good diagnostic efficacy in lots of preclinical studies in multiple tumors, it is limited to a generally small sample size and lacks external validation. There for, a clinical study of Raman spectra for tumor diagnosis is needed, which meets the following requirements: 1.An objective, fast and practical application of Raman spectral data processing is needed and deep learning method may be the best classification method; 2. It requires multicenter and large clinical samples to train deep learning diagnostic model, and verify its true efficacy through external data of prospective study. In preliminary research, the investigators collected serum Raman spectroscopy data from a cohort of 191 patients with pulmonary nodules and developed an intelligent diagnosis system for distinguishing between benign and malignant pulmonary nodules using a machine learning model. The system achieved an accuracy of 89.7%. In order to obtain the highest level of clinical evidence and truly realize clinical transformation, this prospective, multi-center clinical study is designed to verify the intelligent diagnostic system for early diagnosis of prostate cancer.

Sex

All

Minimum age

18 Years

Inclusion Criteria:

1. Participants with Lung cancer meeting the criteria of TNM (Ninth Edition);
2. Participants are willing to participate in this study and follow the research plan;
3. Participants or legally authorized representatives can give written informed consent approved by the Ethics Review Committee that manages the website.

Exclusion Criteria:

1. Participants with concomitant other malignant tumors;
2. Participants with missing baseline clinical data;
3. Participants with severe underlying lung diseases (such as bronchiectasis, bronchial asthma or COPD, etc.), or those with a history of occupational or environmental exposure to dust, mines or asbestos;
4. Participants who do not cooperate or refuse to participate in clinical trials at a later stage.