Clinical Research Directory

SERS Sensor Based on CHA Reaction for EGFR Mutation Typing in Advanced Lung Cancer

Summary:This study is a prospective, multicenter clinical study. In previous studies, we successfully constructed a CHA reaction-mediated self-calibrated SERS biosensor for the detection of EGFR mutation typing (Del-19, T790M, L858R) in lung cancer patients, and verified that the accuracy, sensitivity, and specificity of the SERS biosensor exceeded 95% in a small sample of 32 patients. In order to obtain the highest level of clinical evidence and truly achieve clinical transformation, this prospective, multicenter clinical study aims to verify the analytical efficiency of the SERS biosensor for EGFR mutation typing in patients with advanced lung cancer. Purpose：This prospective, multicenter clinical study aims to verify the analytical efficacy of the previously constructed CHA reaction-mediated self-calibrated SERS biosensor in EGFR mutation typing in patients with advanced lung cancer. Research subjects: The patients enrolled in this project are confirmed to be advanced non-small cell lung cancer (NSCLC). Enrollment will be completed in 25 centers and the enrollment will be competitive. Research location: 900th Hospital of Joint Logistics Support Force Research intervention: None Study duration: Patients will be enrolled from June 2024 to June 2025. Subject participation time: Telephone follow-up will be conducted every three months until the end of the study.

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.