Lung cancer can be divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for about 85% and SCLC about 15%. The prognoses of different types of lung cancer vary significantly. Early identification of different pathological types of lung cancer is crucial to the patient's prognosis.

Raman Spectrum (RS), as a non-invasive and highly specific molecular detection technique, can obtain information at the molecular level, thereby sensitively detecting changes in biomolecules related to tumor metabolism such as proteins, nucleic acids, lipids, and sugars. Surface-enhanced Raman spectroscopy (SERS), developed based on this technology, is one of the feasible methods for high-sensitivity biomolecular analysis.

In preliminary study, the investigators collected serum Raman spectral data from a cohort of 233 patients with malignant lung tumors and built a Raman intelligent diagnostic system for SCLC and NSCLC based on a machine learning model, achieving an accuracy rate of 80%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical study aims to validate the use of this intelligent diagnostic system for the early diagnosis of SCLC.

1. Screening interested participants should sign the appropriate informed consent (ICF) prior to completion any study procedures.
2. The investigator will review symptoms, risk factors, and other non-invasive inclusion and exclusion criteria.
3. Completion of baseline procedures, participants were assessed for 30 days and completed all safety monitoring.
4. After completing the baseline assessment and confirming enrollment, participants will be given 2ml of fasting venous blood.

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 pulmonary diseases (such as bronchiectasis, bronchial asthma, or COPD), or those with a history of occupational or environmental exposure to dust, mines, or asbestos;
4. Participants who are uncooperative or refuse to participate in the clinical trial later on.

Chest CT confirmed the presence of a pulmonary space-occupying lesion, which ultimately led to a lung biopsy or surgical intervention. Pathology indicated a malignant lung tumor.