SERS-Based Serum Molecular Spectral Screening for Benign and Malignant Pulmonary Proliferative Nodules (SERS on lung)

SERS-Based Serum Molecular Spectral Screening for Benign and Malignant Pulmonary Proliferative Nodules: A Multicenter, Open-Label, Double-Blind, Independent Data Analysis Clinical Trial

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Lung Cancer in Normal and Malignant Tumors
• Intervention / Treatment: Diagnostic test: Serum Raman spectroscopy intelligent diagnostic system

Detailed Description

In 2020, there were approximately 19.3 million new cancer cases and nearly 10 million cancer deaths worldwide. Among them, the number of new cases of lung cancer was approximately 2.21 million, accounting for 11.4% of the total number of new cancer cases. There were approximately 1.8 million deaths from lung cancer, ranking first among cancer deaths. In that year, the number of new cases of lung cancer in China was 816,000, accounting for 37% of the global total. In 2022, the latest number of lung cancer cases in China increased to 1.0606 million, and the number of lung cancer deaths was 733,000. In terms of the 5-year survival rate of lung cancer patients, the data released in 2018 in China (2012-2015) was 19.7%, which is still a large gap from the overall cancer 5-year survival rate of 46.6% by 2030 proposed in the "Healthy China Action-Cancer Prevention and Control Action Implementation Plan (2023-2030)". The prognosis of lung cancer at different stages is quite different. The 5-year survival rate of stage I lung cancer is 77%~92%, and the 5-year survival rate of stage IIIA~IVB lung cancer is 0~36%. Therefore, early diagnosis and treatment of lung cancer is the key to improving the 5-year survival rate of lung cancer and improving the prognosis of patients. However, most lung cancer patients are already in the late stage of lung cancer when they are diagnosed, and they have missed the opportunity for radical treatment. The main reason is that the primary and secondary prevention work is not done enough. It is necessary to develop advanced technologies and integrate them into the consensus guidelines for wide promotion.

Pulmonary nodules are early manifestations of lung cancer. With the popularization of chest CT screening in physical examination items, more and more lung nodules are found in physical examinations, including various types of small nodules, such as inflammatory lesions, benign tumor lesions, and malignant tumor lesions. In order to identify these types of nodules, clinicians often judge the two-dimensional imaging features of nodules based on their personal experience, such as plane diameter, whether there are burrs, lobes, calcification and other features to assess the probability of malignancy of lung nodules, but the accuracy of judging the benign and malignant nodules in this way is closely related to the experience and seniority of clinicians, and different doctors have different judgments on the same nodules. At present, there is no unified consensus on the diagnosis and treatment strategies of lung nodules recommended by multiple international consensus guidelines. In public health management facilities, the development and implementation of a comprehensive lung nodule lung cancer screening program is a complex and challenging task. Researching and proposing high-sensitivity and high-specificity, as well as simple, easy-to-popular and low-cost lung cancer screening technologies is an indispensable part of the healthcare system. In addition, due to the inconsistency of guidelines for the diagnosis and treatment strategies of lung nodules, the phenomenon of overdiagnosis and treatment of lung nodules is also common in clinical practice. How to avoid overdiagnosis and treatment needs more attention. Therefore, it is our responsibility to actively improve the accuracy of prediction of lung nodule canceration, reduce the rate of overdiagnosis and treatment, and increase the rate of early lung cancer intervention. Among the existing screening methods for early lung cancer, laboratory tests (especially the use of blood, urine or other liquid biopsies) are a low-cost, non-invasive and easily repeatable early prediction method compared with imaging or histopathological examinations, by detecting specific cancer biomarkers such as circulating tumor DNA, proteins, cancer metabolites, and even cell-derived exosomes and circulating tumor cells. However, there are still many challenges, including: 1) There are no effective and abundant tumor biomarkers for lung cancer; 2) There is no simple and feasible cancer detection method, especially in the asymptomatic stage; 3) There is no comprehensive analysis platform for large data sets to distinguish between healthy and lung cancer populations.

Raman spectroscopy (RS) is a non-invasive and highly specific material molecular detection technology that 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. Surface-enhanced Raman spectroscopy (SERS) developed based on this technology is one of the feasible methods for highly sensitive biomolecular analysis technology. Although SERS technology has shown good diagnostic effects in a large number of preclinical studies of multiple tumors, it is limited by the generally small sample size and lack of external verification. Therefore, it is necessary to conduct clinical research on the use of Raman spectroscopy for tumor diagnosis, which meets the following requirements: 1. Objective, fast and practical Raman spectroscopy data processing methods are required, and machine and deep learning methods may be the best classification methods; 2. Multi-center, large-sample clinical samples are needed to train deep learning diagnostic models, and their true efficacy is verified by external data from prospective studies.

In previous study, the investigators collected serum Raman spectroscopy data from a cohort of 191 patients with pulmonary nodules, and built a Raman intelligent diagnosis system for benign and malignant pulmonary nodules based on a machine learning model. The accuracy of this intelligent diagnosis system reached 89.7%. In order to obtain the highest level of clinical evidence and truly achieve clinical transformation, this prospective, multi-center clinical study aims to verify the use of this intelligent diagnosis system for the early diagnosis of malignant pulmonary nodules.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Zongyang Yu, Ph.D; Phone Number: 13509327806; Email: yuzy527@sina.com

Study Locations

• China
  • Fujian
    • Fuzhou, Fujian, China, 350000
      • Raman detector
      • Contact: Zongyang Yu, degree; Phone Number: 0591-22859650; Email: yuzy525@sina.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Chest CT reveals the presence of pulmonary nodules in the patient and plans to undergo surgical treatment

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Chest CT confirms patient with pulmonary nodules; Chest CT confirmed the presence of pulmonary nodules in the patient and ultimately underwent surgical intervention. The pulmonary nodules had the final pathological results.

Diagnostic test: Serum Raman spectroscopy intelligent diagnostic system; Screening interested participants should sign the appropriate informed consent (ICF) prior to completion any study procedures.; The investigator will review symptoms, risk factors, and other non-invasive inclusion and exclusion criteria.; The following is the general sequence of events during the 3 months evaluation period:; Completion of baseline procedures Participants were assessed for 3 months and completed all safety monitoring.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic accuracy	Determine whether there is hematogenous metastasis in enrolled lung cancer patients through RAMAN intelligent diagnostic system	through study completion, an average of 1 year
Postoperative pathological results	After undergoing surgical resection of pulmonary nodules, the final pathological nature of the pulmonary nodules was determined through pathological examination.	through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time to RAMAN diagnosis	The time to perform RAMAN testing and obtain diagnostic results after obtaining serum	up to 30 days
Safety assessment Results	AEs and SAEs through Day 30	up to 30 days

Collaborators and Investigators

• Sponsor: Fuzhou General Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): April 8, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: December 24, 2024
• First Submitted That Met QC Criteria: January 9, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 31, 2025
• Last Update Submitted That Met QC Criteria: March 26, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: Pulmonary nodule; Raman; SERS; diagnostic model; Early screening for lung cancer
• Additional Relevant MeSH Terms: Neoplasms
• Other Study ID Numbers: 2024-041

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Age, gender, smoking history, and tumor type of the enrolled patients
• IPD Sharing Access Criteria: For any reasonable needs related to scientific research, please contact the project leader for specific data consultation.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No